Overview

Dataset statistics

Number of variables48
Number of observations817723
Missing cells8268003
Missing cells (%)21.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory299.5 MiB
Average record size in memory384.0 B

Variable types

Text3
Boolean9
DateTime2
Numeric15
Categorical19

Alerts

INJURIES_UNKNOWN has constant value ""Constant
TRAFFIC_CONTROL_DEVICE is highly imbalanced (61.7%)Imbalance
DEVICE_CONDITION is highly imbalanced (54.4%)Imbalance
WEATHER_CONDITION is highly imbalanced (66.2%)Imbalance
ALIGNMENT is highly imbalanced (92.0%)Imbalance
ROADWAY_SURFACE_COND is highly imbalanced (55.3%)Imbalance
ROAD_DEFECT is highly imbalanced (69.9%)Imbalance
INTERSECTION_RELATED_I is highly imbalanced (72.4%)Imbalance
NOT_RIGHT_OF_WAY_I is highly imbalanced (55.8%)Imbalance
HIT_AND_RUN_I is highly imbalanced (74.4%)Imbalance
SEC_CONTRIBUTORY_CAUSE is highly imbalanced (54.2%)Imbalance
MOST_SEVERE_INJURY is highly imbalanced (66.7%)Imbalance
INJURIES_FATAL is highly imbalanced (99.4%)Imbalance
CRASH_DATE_EST_I has 756594 (92.5%) missing valuesMissing
LANE_CNT has 618714 (75.7%) missing valuesMissing
REPORT_TYPE has 24314 (3.0%) missing valuesMissing
INTERSECTION_RELATED_I has 630174 (77.1%) missing valuesMissing
NOT_RIGHT_OF_WAY_I has 780015 (95.4%) missing valuesMissing
HIT_AND_RUN_I has 561774 (68.7%) missing valuesMissing
PHOTOS_TAKEN_I has 806948 (98.7%) missing valuesMissing
STATEMENTS_TAKEN_I has 799465 (97.8%) missing valuesMissing
DOORING_I has 815211 (99.7%) missing valuesMissing
WORK_ZONE_I has 813053 (99.4%) missing valuesMissing
WORK_ZONE_TYPE has 814105 (99.6%) missing valuesMissing
WORKERS_PRESENT_I has 816529 (99.9%) missing valuesMissing
LANE_CNT is highly skewed (γ1 = 350.3035967)Skewed
LATITUDE is highly skewed (γ1 = -116.2686453)Skewed
LONGITUDE is highly skewed (γ1 = 127.289911)Skewed
CRASH_RECORD_ID has unique valuesUnique
INJURIES_TOTAL has 703431 (86.0%) zerosZeros
INJURIES_INCAPACITATING has 801987 (98.1%) zerosZeros
INJURIES_NON_INCAPACITATING has 749965 (91.7%) zerosZeros
INJURIES_REPORTED_NOT_EVIDENT has 777695 (95.1%) zerosZeros
INJURIES_NO_INDICATION has 17119 (2.1%) zerosZeros
CRASH_HOUR has 17728 (2.2%) zerosZeros

Reproduction

Analysis started2024-04-08 03:21:36.936427
Analysis finished2024-04-08 03:25:45.030023
Duration4 minutes and 8.09 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

CRASH_RECORD_ID
Text

UNIQUE 

Distinct817723
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
2024-04-07T23:25:45.339407image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length128
Median length128
Mean length128
Min length128

Characters and Unicode

Total characters104668544
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique817723 ?
Unique (%)100.0%

Sample

1st row6c1659069e9c6285a650e70d6f9b574ed5f64c12888479093dfeef179c0344ec6d2057eae224b5c0d5dfc278c0a237f8c22543f07fdef2e4a95a3849871c9345
2nd row5f54a59fcb087b12ae5b1acff96a3caf4f2d37e79f8db4106558b34b8a6d2b81af02cf91b576ecd7ced08ffd10fcfd940a84f7613125b89d33636e6075064e22
3rd row61fcb8c1eb522a6469b460e2134df3d15f82e81fd93e9cafd3dc7e631b9e1ba8b450a63af12bd90d1d2d9b127ea287f88d32e138a4eeba17799f536c08048934
4th row004cd14d0303a9163aad69a2d7f341b7da2a8572b2ab3378594bfae8ac53dcb604dd8d414f93c290b55862f9f2517ad32e6209cbc8034c2e26eb3c2bc9724390
5th rowa1d5f0ea90897745365a4cbb06cc60329a120d89753fac2b02d69c9685d9cf7c763870a60abd01484a39ed1e6c09b1ba59f38214c03a83cccde1247f794e0287
ValueCountFrequency (%)
6c1659069e9c6285a650e70d6f9b574ed5f64c12888479093dfeef179c0344ec6d2057eae224b5c0d5dfc278c0a237f8c22543f07fdef2e4a95a3849871c9345 1
 
< 0.1%
569af2dc0eaa9c5a55a01f1d5fd678644c3dd76d586b51f7b87b1b5c6c3cf8700b10a9fea3c98cfc8df295c8b4db1fe28c4209e34e3a021b50c56023c798d1f9 1
 
< 0.1%
4a1f7a24129e5e1d4a7a2fd44ab6f8822a20bcdb2f627f490af8975d8c14e9fadf2d7b533a09a7e8c2cc57a4b3563803d7d4eb4db3892337cf233f1f0f154013 1
 
< 0.1%
57cfcdd70be67a9d66fd973426036aa20034e0f99b695207149cc51002a8c53d7801b1860869929c065660c6f3afa874bb36eb868784218dc931993ba7cd4bf7 1
 
< 0.1%
1ee2180a89cc02c0b756f95b5b2755bb5cc9d93450f5caafaee5cb5c6aa75adafd251402ac205833ff78be9c9a0eb799f33d9c854975cf3a8e10c6a7d28116c7 1
 
< 0.1%
61fcb8c1eb522a6469b460e2134df3d15f82e81fd93e9cafd3dc7e631b9e1ba8b450a63af12bd90d1d2d9b127ea287f88d32e138a4eeba17799f536c08048934 1
 
< 0.1%
004cd14d0303a9163aad69a2d7f341b7da2a8572b2ab3378594bfae8ac53dcb604dd8d414f93c290b55862f9f2517ad32e6209cbc8034c2e26eb3c2bc9724390 1
 
< 0.1%
a1d5f0ea90897745365a4cbb06cc60329a120d89753fac2b02d69c9685d9cf7c763870a60abd01484a39ed1e6c09b1ba59f38214c03a83cccde1247f794e0287 1
 
< 0.1%
b236c77d59e32b7b469a6e2f17f438b7457e1bd8bc689b14cb4f5b1590cbe784f2b9e554b41925797251cbd3e93a3f4a131d1923b327673d441ae79c052f79c2 1
 
< 0.1%
35156ce97cab22747495e92e8bbb16c57e0e60dc3ce6d1f1852f2f7cece07c7ae825b073b286b1da52dfa58082ff6d763ecf1f13f06a223c7aed2b6c1e8c5972 1
 
< 0.1%
Other values (817713) 817713
> 99.9%
2024-04-07T23:25:45.704847image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 6547788
 
6.3%
b 6545782
 
6.3%
d 6545680
 
6.3%
f 6545495
 
6.3%
c 6544094
 
6.3%
9 6542979
 
6.3%
0 6542034
 
6.3%
4 6541970
 
6.3%
8 6541267
 
6.2%
6 6540678
 
6.2%
Other values (6) 39230777
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 104668544
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 6547788
 
6.3%
b 6545782
 
6.3%
d 6545680
 
6.3%
f 6545495
 
6.3%
c 6544094
 
6.3%
9 6542979
 
6.3%
0 6542034
 
6.3%
4 6541970
 
6.3%
8 6541267
 
6.2%
6 6540678
 
6.2%
Other values (6) 39230777
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 104668544
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 6547788
 
6.3%
b 6545782
 
6.3%
d 6545680
 
6.3%
f 6545495
 
6.3%
c 6544094
 
6.3%
9 6542979
 
6.3%
0 6542034
 
6.3%
4 6541970
 
6.3%
8 6541267
 
6.2%
6 6540678
 
6.2%
Other values (6) 39230777
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 104668544
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 6547788
 
6.3%
b 6545782
 
6.3%
d 6545680
 
6.3%
f 6545495
 
6.3%
c 6544094
 
6.3%
9 6542979
 
6.3%
0 6542034
 
6.3%
4 6541970
 
6.3%
8 6541267
 
6.2%
6 6540678
 
6.2%
Other values (6) 39230777
37.5%

CRASH_DATE_EST_I
Boolean

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing756594
Missing (%)92.5%
Memory size1.6 MiB
True
 
53267
False
 
7862
(Missing)
756594 
ValueCountFrequency (%)
True 53267
 
6.5%
False 7862
 
1.0%
(Missing) 756594
92.5%
2024-04-07T23:25:45.777192image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Distinct536888
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
Minimum2013-03-03 16:48:00
Maximum2024-03-26 01:40:00
2024-04-07T23:25:45.833604image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:45.903947image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

POSTED_SPEED_LIMIT
Real number (ℝ)

Distinct46
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.406041
Minimum0
Maximum99
Zeros7437
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-04-07T23:25:45.979052image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q130
median30
Q330
95-th percentile35
Maximum99
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.1660655
Coefficient of variation (CV)0.21706881
Kurtosis7.6315703
Mean28.406041
Median Absolute Deviation (MAD)0
Skewness-1.8200476
Sum23228273
Variance38.020363
MonotonicityNot monotonic
2024-04-07T23:25:46.052736image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
30 601823
73.6%
35 54652
 
6.7%
25 51779
 
6.3%
20 34071
 
4.2%
15 29084
 
3.6%
10 19118
 
2.3%
40 7811
 
1.0%
0 7437
 
0.9%
45 5446
 
0.7%
5 4711
 
0.6%
Other values (36) 1791
 
0.2%
ValueCountFrequency (%)
0 7437
0.9%
1 41
 
< 0.1%
2 28
 
< 0.1%
3 199
 
< 0.1%
4 2
 
< 0.1%
5 4711
0.6%
6 7
 
< 0.1%
7 5
 
< 0.1%
8 2
 
< 0.1%
9 96
 
< 0.1%
ValueCountFrequency (%)
99 66
 
< 0.1%
70 6
 
< 0.1%
65 18
 
< 0.1%
63 1
 
< 0.1%
62 1
 
< 0.1%
60 50
 
< 0.1%
55 808
0.1%
50 243
 
< 0.1%
49 1
 
< 0.1%
46 1
 
< 0.1%

TRAFFIC_CONTROL_DEVICE
Categorical

IMBALANCE 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
NO CONTROLS
464816 
TRAFFIC SIGNAL
226679 
STOP SIGN/FLASHER
81158 
UNKNOWN
 
32812
OTHER
 
5550
Other values (14)
 
6708

Length

Max length24
Median length11
Mean length12.259755
Min length5

Characters and Unicode

Total characters10025084
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOTHER
2nd rowTRAFFIC SIGNAL
3rd rowNO CONTROLS
4th rowNO CONTROLS
5th rowNO CONTROLS

Common Values

ValueCountFrequency (%)
NO CONTROLS 464816
56.8%
TRAFFIC SIGNAL 226679
27.7%
STOP SIGN/FLASHER 81158
 
9.9%
UNKNOWN 32812
 
4.0%
OTHER 5550
 
0.7%
LANE USE MARKING 1226
 
0.1%
YIELD 1207
 
0.1%
OTHER REG. SIGN 908
 
0.1%
OTHER WARNING SIGN 669
 
0.1%
RAILROAD CROSSING GATE 532
 
0.1%
Other values (9) 2166
 
0.3%

Length

2024-04-07T23:25:46.125890image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 464867
29.1%
controls 464816
29.1%
signal 227007
14.2%
traffic 226679
14.2%
stop 81158
 
5.1%
sign/flasher 81158
 
5.1%
unknown 32812
 
2.1%
other 7308
 
0.5%
sign 2275
 
0.1%
crossing 1411
 
0.1%
Other values (18) 10328
 
0.6%

Most occurring characters

ValueCountFrequency (%)
O 1520081
15.2%
N 1345892
13.4%
S 943198
9.4%
R 787081
7.9%
782096
7.8%
T 781661
7.8%
L 777993
7.8%
C 693869
6.9%
I 543866
 
5.4%
A 541696
 
5.4%
Other values (15) 1307651
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10025084
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 1520081
15.2%
N 1345892
13.4%
S 943198
9.4%
R 787081
7.9%
782096
7.8%
T 781661
7.8%
L 777993
7.8%
C 693869
6.9%
I 543866
 
5.4%
A 541696
 
5.4%
Other values (15) 1307651
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10025084
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 1520081
15.2%
N 1345892
13.4%
S 943198
9.4%
R 787081
7.9%
782096
7.8%
T 781661
7.8%
L 777993
7.8%
C 693869
6.9%
I 543866
 
5.4%
A 541696
 
5.4%
Other values (15) 1307651
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10025084
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 1520081
15.2%
N 1345892
13.4%
S 943198
9.4%
R 787081
7.9%
782096
7.8%
T 781661
7.8%
L 777993
7.8%
C 693869
6.9%
I 543866
 
5.4%
A 541696
 
5.4%
Other values (15) 1307651
13.0%

DEVICE_CONDITION
Categorical

IMBALANCE 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
NO CONTROLS
470240 
FUNCTIONING PROPERLY
280006 
UNKNOWN
54603 
OTHER
 
6264
FUNCTIONING IMPROPERLY
 
3849
Other values (3)
 
2761

Length

Max length24
Median length11
Mean length13.836213
Min length5

Characters and Unicode

Total characters11314190
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFUNCTIONING PROPERLY
2nd rowFUNCTIONING PROPERLY
3rd rowNO CONTROLS
4th rowNO CONTROLS
5th rowNO CONTROLS

Common Values

ValueCountFrequency (%)
NO CONTROLS 470240
57.5%
FUNCTIONING PROPERLY 280006
34.2%
UNKNOWN 54603
 
6.7%
OTHER 6264
 
0.8%
FUNCTIONING IMPROPERLY 3849
 
0.5%
NOT FUNCTIONING 2382
 
0.3%
WORN REFLECTIVE MATERIAL 284
 
< 0.1%
MISSING 95
 
< 0.1%

Length

2024-04-07T23:25:46.197800image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T23:25:46.277350image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
no 470240
29.9%
controls 470240
29.9%
functioning 286237
18.2%
properly 280006
17.8%
unknown 54603
 
3.5%
other 6264
 
0.4%
improperly 3849
 
0.2%
not 2382
 
0.2%
worn 284
 
< 0.1%
reflective 284
 
< 0.1%
Other values (2) 379
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
O 2044345
18.1%
N 1965761
17.4%
R 1045066
9.2%
T 765691
 
6.8%
757045
 
6.7%
C 756761
 
6.7%
L 754663
 
6.7%
I 577081
 
5.1%
P 567710
 
5.0%
S 470430
 
4.2%
Other values (11) 1609637
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11314190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 2044345
18.1%
N 1965761
17.4%
R 1045066
9.2%
T 765691
 
6.8%
757045
 
6.7%
C 756761
 
6.7%
L 754663
 
6.7%
I 577081
 
5.1%
P 567710
 
5.0%
S 470430
 
4.2%
Other values (11) 1609637
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11314190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 2044345
18.1%
N 1965761
17.4%
R 1045066
9.2%
T 765691
 
6.8%
757045
 
6.7%
C 756761
 
6.7%
L 754663
 
6.7%
I 577081
 
5.1%
P 567710
 
5.0%
S 470430
 
4.2%
Other values (11) 1609637
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11314190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 2044345
18.1%
N 1965761
17.4%
R 1045066
9.2%
T 765691
 
6.8%
757045
 
6.7%
C 756761
 
6.7%
L 754663
 
6.7%
I 577081
 
5.1%
P 567710
 
5.0%
S 470430
 
4.2%
Other values (11) 1609637
14.2%

WEATHER_CONDITION
Categorical

IMBALANCE 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
CLEAR
641373 
RAIN
71391 
UNKNOWN
 
45108
SNOW
 
28352
CLOUDY/OVERCAST
 
24249
Other values (7)
 
7250

Length

Max length24
Median length5
Mean length5.3462053
Min length4

Characters and Unicode

Total characters4371715
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCLEAR
2nd rowCLEAR
3rd rowCLEAR
4th rowCLEAR
5th rowCLEAR

Common Values

ValueCountFrequency (%)
CLEAR 641373
78.4%
RAIN 71391
 
8.7%
UNKNOWN 45108
 
5.5%
SNOW 28352
 
3.5%
CLOUDY/OVERCAST 24249
 
3.0%
OTHER 2601
 
0.3%
FREEZING RAIN/DRIZZLE 1702
 
0.2%
FOG/SMOKE/HAZE 1337
 
0.2%
SLEET/HAIL 1006
 
0.1%
BLOWING SNOW 444
 
0.1%
Other values (2) 160
 
< 0.1%

Length

2024-04-07T23:25:46.362160image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
clear 641373
78.2%
rain 71391
 
8.7%
unknown 45108
 
5.5%
snow 28796
 
3.5%
cloudy/overcast 24249
 
3.0%
other 2601
 
0.3%
freezing 1702
 
0.2%
rain/drizzle 1702
 
0.2%
fog/smoke/haze 1337
 
0.2%
sleet/hail 1006
 
0.1%
Other values (8) 1084
 
0.1%

Most occurring characters

ValueCountFrequency (%)
R 745033
17.0%
A 741218
17.0%
C 690024
15.8%
E 678627
15.5%
L 669794
15.3%
N 239526
 
5.5%
O 128288
 
2.9%
I 78121
 
1.8%
W 74508
 
1.7%
U 69357
 
1.6%
Other values (15) 257219
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4371715
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 745033
17.0%
A 741218
17.0%
C 690024
15.8%
E 678627
15.5%
L 669794
15.3%
N 239526
 
5.5%
O 128288
 
2.9%
I 78121
 
1.8%
W 74508
 
1.7%
U 69357
 
1.6%
Other values (15) 257219
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4371715
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 745033
17.0%
A 741218
17.0%
C 690024
15.8%
E 678627
15.5%
L 669794
15.3%
N 239526
 
5.5%
O 128288
 
2.9%
I 78121
 
1.8%
W 74508
 
1.7%
U 69357
 
1.6%
Other values (15) 257219
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4371715
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 745033
17.0%
A 741218
17.0%
C 690024
15.8%
E 678627
15.5%
L 669794
15.3%
N 239526
 
5.5%
O 128288
 
2.9%
I 78121
 
1.8%
W 74508
 
1.7%
U 69357
 
1.6%
Other values (15) 257219
 
5.9%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
DAYLIGHT
522973 
DARKNESS, LIGHTED ROAD
181030 
DARKNESS
 
39092
UNKNOWN
 
37360
DUSK
 
23579

Length

Max length22
Median length8
Mean length10.871373
Min length4

Characters and Unicode

Total characters8889772
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDAYLIGHT
2nd rowDAYLIGHT
3rd rowDAYLIGHT
4th rowDAYLIGHT
5th rowDAYLIGHT

Common Values

ValueCountFrequency (%)
DAYLIGHT 522973
64.0%
DARKNESS, LIGHTED ROAD 181030
 
22.1%
DARKNESS 39092
 
4.8%
UNKNOWN 37360
 
4.6%
DUSK 23579
 
2.9%
DAWN 13689
 
1.7%

Length

2024-04-07T23:25:46.438938image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T23:25:46.522316image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
daylight 522973
44.3%
darkness 220122
18.7%
lighted 181030
 
15.3%
road 181030
 
15.3%
unknown 37360
 
3.2%
dusk 23579
 
2.0%
dawn 13689
 
1.2%

Most occurring characters

ValueCountFrequency (%)
D 1142423
12.9%
A 937814
10.5%
L 704003
 
7.9%
I 704003
 
7.9%
G 704003
 
7.9%
H 704003
 
7.9%
T 704003
 
7.9%
Y 522973
 
5.9%
S 463823
 
5.2%
R 401152
 
4.5%
Other values (8) 1901572
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8889772
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 1142423
12.9%
A 937814
10.5%
L 704003
 
7.9%
I 704003
 
7.9%
G 704003
 
7.9%
H 704003
 
7.9%
T 704003
 
7.9%
Y 522973
 
5.9%
S 463823
 
5.2%
R 401152
 
4.5%
Other values (8) 1901572
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8889772
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 1142423
12.9%
A 937814
10.5%
L 704003
 
7.9%
I 704003
 
7.9%
G 704003
 
7.9%
H 704003
 
7.9%
T 704003
 
7.9%
Y 522973
 
5.9%
S 463823
 
5.2%
R 401152
 
4.5%
Other values (8) 1901572
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8889772
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 1142423
12.9%
A 937814
10.5%
L 704003
 
7.9%
I 704003
 
7.9%
G 704003
 
7.9%
H 704003
 
7.9%
T 704003
 
7.9%
Y 522973
 
5.9%
S 463823
 
5.2%
R 401152
 
4.5%
Other values (8) 1901572
21.4%

FIRST_CRASH_TYPE
Categorical

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
PARKED MOTOR VEHICLE
190062 
REAR END
182653 
SIDESWIPE SAME DIRECTION
124798 
TURNING
116863 
ANGLE
88951 
Other values (13)
114396 

Length

Max length28
Median length20
Mean length13.480296
Min length5

Characters and Unicode

Total characters11023148
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowREAR END
2nd rowPARKED MOTOR VEHICLE
3rd rowPEDALCYCLIST
4th rowPEDESTRIAN
5th rowFIXED OBJECT

Common Values

ValueCountFrequency (%)
PARKED MOTOR VEHICLE 190062
23.2%
REAR END 182653
22.3%
SIDESWIPE SAME DIRECTION 124798
15.3%
TURNING 116863
14.3%
ANGLE 88951
10.9%
FIXED OBJECT 38417
 
4.7%
PEDESTRIAN 19065
 
2.3%
PEDALCYCLIST 12307
 
1.5%
SIDESWIPE OPPOSITE DIRECTION 11529
 
1.4%
OTHER OBJECT 8102
 
1.0%
Other values (8) 24976
 
3.1%

Length

2024-04-07T23:25:46.595383image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rear 198570
11.4%
parked 190062
10.9%
motor 190062
10.9%
vehicle 190062
10.9%
end 182653
10.5%
sideswipe 136327
7.8%
direction 136327
7.8%
same 124798
7.2%
turning 116863
6.7%
angle 88951
5.1%
Other values (15) 183121
10.5%

Most occurring characters

ValueCountFrequency (%)
E 1744409
15.8%
R 1069188
9.7%
I 944043
 
8.6%
920073
 
8.3%
D 727345
 
6.6%
N 684515
 
6.2%
A 641957
 
5.8%
O 634149
 
5.8%
T 566121
 
5.1%
S 447634
 
4.1%
Other values (15) 2643714
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11023148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1744409
15.8%
R 1069188
9.7%
I 944043
 
8.6%
920073
 
8.3%
D 727345
 
6.6%
N 684515
 
6.2%
A 641957
 
5.8%
O 634149
 
5.8%
T 566121
 
5.1%
S 447634
 
4.1%
Other values (15) 2643714
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11023148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1744409
15.8%
R 1069188
9.7%
I 944043
 
8.6%
920073
 
8.3%
D 727345
 
6.6%
N 684515
 
6.2%
A 641957
 
5.8%
O 634149
 
5.8%
T 566121
 
5.1%
S 447634
 
4.1%
Other values (15) 2643714
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11023148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1744409
15.8%
R 1069188
9.7%
I 944043
 
8.6%
920073
 
8.3%
D 727345
 
6.6%
N 684515
 
6.2%
A 641957
 
5.8%
O 634149
 
5.8%
T 566121
 
5.1%
S 447634
 
4.1%
Other values (15) 2643714
24.0%

TRAFFICWAY_TYPE
Categorical

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
NOT DIVIDED
355079 
DIVIDED - W/MEDIAN (NOT RAISED)
130679 
ONE-WAY
104273 
PARKING LOT
55690 
FOUR WAY
51707 
Other values (15)
120295 

Length

Max length31
Median length11
Mean length14.145716
Min length4

Characters and Unicode

Total characters11567277
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOTHER
2nd rowDIVIDED - W/MEDIAN (NOT RAISED)
3rd rowNOT DIVIDED
4th rowONE-WAY
5th rowOTHER

Common Values

ValueCountFrequency (%)
NOT DIVIDED 355079
43.4%
DIVIDED - W/MEDIAN (NOT RAISED) 130679
 
16.0%
ONE-WAY 104273
 
12.8%
PARKING LOT 55690
 
6.8%
FOUR WAY 51707
 
6.3%
DIVIDED - W/MEDIAN BARRIER 46724
 
5.7%
OTHER 22378
 
2.7%
ALLEY 13526
 
1.7%
T-INTERSECTION 10407
 
1.3%
UNKNOWN 9520
 
1.2%
Other values (10) 17740
 
2.2%

Length

2024-04-07T23:25:46.668260image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
divided 532482
27.1%
not 486338
24.8%
177403
 
9.0%
w/median 177403
 
9.0%
raised 130679
 
6.7%
one-way 104273
 
5.3%
parking 55690
 
2.8%
lot 55690
 
2.8%
four 51707
 
2.6%
way 51707
 
2.6%
Other values (22) 141247
 
7.2%

Most occurring characters

ValueCountFrequency (%)
D 1909026
16.5%
I 1509514
13.0%
1146896
9.9%
E 1082853
9.4%
N 906834
7.8%
O 751820
 
6.5%
T 621099
 
5.4%
A 592334
 
5.1%
V 536266
 
4.6%
R 437333
 
3.8%
Other values (18) 2073302
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11567277
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 1909026
16.5%
I 1509514
13.0%
1146896
9.9%
E 1082853
9.4%
N 906834
7.8%
O 751820
 
6.5%
T 621099
 
5.4%
A 592334
 
5.1%
V 536266
 
4.6%
R 437333
 
3.8%
Other values (18) 2073302
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11567277
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 1909026
16.5%
I 1509514
13.0%
1146896
9.9%
E 1082853
9.4%
N 906834
7.8%
O 751820
 
6.5%
T 621099
 
5.4%
A 592334
 
5.1%
V 536266
 
4.6%
R 437333
 
3.8%
Other values (18) 2073302
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11567277
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 1909026
16.5%
I 1509514
13.0%
1146896
9.9%
E 1082853
9.4%
N 906834
7.8%
O 751820
 
6.5%
T 621099
 
5.4%
A 592334
 
5.1%
V 536266
 
4.6%
R 437333
 
3.8%
Other values (18) 2073302
17.9%

LANE_CNT
Real number (ℝ)

MISSING  SKEWED 

Distinct41
Distinct (%)< 0.1%
Missing618714
Missing (%)75.7%
Infinite0
Infinite (%)0.0%
Mean13.330161
Minimum0
Maximum1191625
Zeros8032
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-04-07T23:25:46.743465image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q34
95-th percentile4
Maximum1191625
Range1191625
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2961.6012
Coefficient of variation (CV)222.17295
Kurtosis134678.59
Mean13.330161
Median Absolute Deviation (MAD)1
Skewness350.3036
Sum2652822
Variance8771081.4
MonotonicityNot monotonic
2024-04-07T23:25:46.832614image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
2 91154
 
11.1%
4 49588
 
6.1%
1 32547
 
4.0%
3 8677
 
1.1%
0 8032
 
1.0%
6 4502
 
0.6%
5 1940
 
0.2%
8 1908
 
0.2%
7 184
 
< 0.1%
10 162
 
< 0.1%
Other values (31) 315
 
< 0.1%
(Missing) 618714
75.7%
ValueCountFrequency (%)
0 8032
 
1.0%
1 32547
 
4.0%
2 91154
11.1%
3 8677
 
1.1%
4 49588
6.1%
5 1940
 
0.2%
6 4502
 
0.6%
7 184
 
< 0.1%
8 1908
 
0.2%
9 66
 
< 0.1%
ValueCountFrequency (%)
1191625 1
 
< 0.1%
433634 1
 
< 0.1%
299679 1
 
< 0.1%
218474 1
 
< 0.1%
902 1
 
< 0.1%
400 1
 
< 0.1%
100 2
 
< 0.1%
99 108
< 0.1%
80 1
 
< 0.1%
60 3
 
< 0.1%

ALIGNMENT
Categorical

IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
STRAIGHT AND LEVEL
797888 
STRAIGHT ON GRADE
 
10191
CURVE, LEVEL
 
5904
STRAIGHT ON HILLCREST
 
2145
CURVE ON GRADE
 
1231

Length

Max length21
Median length18
Mean length17.946065
Min length12

Characters and Unicode

Total characters14674910
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSTRAIGHT AND LEVEL
2nd rowSTRAIGHT AND LEVEL
3rd rowSTRAIGHT AND LEVEL
4th rowCURVE ON GRADE
5th rowSTRAIGHT AND LEVEL

Common Values

ValueCountFrequency (%)
STRAIGHT AND LEVEL 797888
97.6%
STRAIGHT ON GRADE 10191
 
1.2%
CURVE, LEVEL 5904
 
0.7%
STRAIGHT ON HILLCREST 2145
 
0.3%
CURVE ON GRADE 1231
 
0.2%
CURVE ON HILLCREST 364
 
< 0.1%

Length

2024-04-07T23:25:46.909885image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T23:25:46.997033image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
straight 810224
33.1%
level 803792
32.8%
and 797888
32.6%
on 13931
 
0.6%
grade 11422
 
0.5%
curve 7499
 
0.3%
hillcrest 2509
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1629542
11.1%
E 1629014
11.1%
T 1622957
11.1%
A 1619534
11.0%
L 1612602
11.0%
R 831654
 
5.7%
G 821646
 
5.6%
S 812733
 
5.5%
I 812733
 
5.5%
H 812733
 
5.5%
Other values (7) 2469762
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14674910
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1629542
11.1%
E 1629014
11.1%
T 1622957
11.1%
A 1619534
11.0%
L 1612602
11.0%
R 831654
 
5.7%
G 821646
 
5.6%
S 812733
 
5.5%
I 812733
 
5.5%
H 812733
 
5.5%
Other values (7) 2469762
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14674910
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1629542
11.1%
E 1629014
11.1%
T 1622957
11.1%
A 1619534
11.0%
L 1612602
11.0%
R 831654
 
5.7%
G 821646
 
5.6%
S 812733
 
5.5%
I 812733
 
5.5%
H 812733
 
5.5%
Other values (7) 2469762
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14674910
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1629542
11.1%
E 1629014
11.1%
T 1622957
11.1%
A 1619534
11.0%
L 1612602
11.0%
R 831654
 
5.7%
G 821646
 
5.6%
S 812733
 
5.5%
I 812733
 
5.5%
H 812733
 
5.5%
Other values (7) 2469762
16.8%

ROADWAY_SURFACE_COND
Categorical

IMBALANCE 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
DRY
603295 
WET
108880 
UNKNOWN
69415 
SNOW OR SLUSH
 
28111
ICE
 
5658
Other values (2)
 
2364

Length

Max length15
Median length3
Mean length3.6928116
Min length3

Characters and Unicode

Total characters3019697
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDRY
2nd rowDRY
3rd rowDRY
4th rowDRY
5th rowDRY

Common Values

ValueCountFrequency (%)
DRY 603295
73.8%
WET 108880
 
13.3%
UNKNOWN 69415
 
8.5%
SNOW OR SLUSH 28111
 
3.4%
ICE 5658
 
0.7%
OTHER 2061
 
0.3%
SAND, MUD, DIRT 303
 
< 0.1%

Length

2024-04-07T23:25:47.075699image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T23:25:47.170639image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
dry 603295
69.0%
wet 108880
 
12.4%
unknown 69415
 
7.9%
snow 28111
 
3.2%
or 28111
 
3.2%
slush 28111
 
3.2%
ice 5658
 
0.6%
other 2061
 
0.2%
sand 303
 
< 0.1%
mud 303
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
R 633770
21.0%
D 604204
20.0%
Y 603295
20.0%
N 236659
 
7.8%
W 206406
 
6.8%
O 127698
 
4.2%
E 116599
 
3.9%
T 111244
 
3.7%
U 97829
 
3.2%
S 84636
 
2.8%
Other values (9) 197357
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3019697
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 633770
21.0%
D 604204
20.0%
Y 603295
20.0%
N 236659
 
7.8%
W 206406
 
6.8%
O 127698
 
4.2%
E 116599
 
3.9%
T 111244
 
3.7%
U 97829
 
3.2%
S 84636
 
2.8%
Other values (9) 197357
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3019697
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 633770
21.0%
D 604204
20.0%
Y 603295
20.0%
N 236659
 
7.8%
W 206406
 
6.8%
O 127698
 
4.2%
E 116599
 
3.9%
T 111244
 
3.7%
U 97829
 
3.2%
S 84636
 
2.8%
Other values (9) 197357
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3019697
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 633770
21.0%
D 604204
20.0%
Y 603295
20.0%
N 236659
 
7.8%
W 206406
 
6.8%
O 127698
 
4.2%
E 116599
 
3.9%
T 111244
 
3.7%
U 97829
 
3.2%
S 84636
 
2.8%
Other values (9) 197357
 
6.5%

ROAD_DEFECT
Categorical

IMBALANCE 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
NO DEFECTS
657425 
UNKNOWN
144337 
RUT, HOLES
 
6056
OTHER
 
4458
WORN SURFACE
 
3356
Other values (2)
 
2091

Length

Max length17
Median length10
Mean length9.465709
Min length5

Characters and Unicode

Total characters7740328
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO DEFECTS
2nd rowNO DEFECTS
3rd rowNO DEFECTS
4th rowNO DEFECTS
5th rowNO DEFECTS

Common Values

ValueCountFrequency (%)
NO DEFECTS 657425
80.4%
UNKNOWN 144337
 
17.7%
RUT, HOLES 6056
 
0.7%
OTHER 4458
 
0.5%
WORN SURFACE 3356
 
0.4%
SHOULDER DEFECT 1475
 
0.2%
DEBRIS ON ROADWAY 616
 
0.1%

Length

2024-04-07T23:25:47.252410image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T23:25:47.341038image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
no 657425
44.2%
defects 657425
44.2%
unknown 144337
 
9.7%
rut 6056
 
0.4%
holes 6056
 
0.4%
other 4458
 
0.3%
worn 3356
 
0.2%
surface 3356
 
0.2%
shoulder 1475
 
0.1%
defect 1475
 
0.1%
Other values (3) 1848
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E 1333761
17.2%
N 1094408
14.1%
O 818339
10.6%
669544
8.7%
T 669414
8.6%
S 668928
8.6%
F 662256
8.6%
C 662256
8.6%
D 661607
8.5%
U 155224
 
2.0%
Other values (10) 344591
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7740328
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1333761
17.2%
N 1094408
14.1%
O 818339
10.6%
669544
8.7%
T 669414
8.6%
S 668928
8.6%
F 662256
8.6%
C 662256
8.6%
D 661607
8.5%
U 155224
 
2.0%
Other values (10) 344591
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7740328
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1333761
17.2%
N 1094408
14.1%
O 818339
10.6%
669544
8.7%
T 669414
8.6%
S 668928
8.6%
F 662256
8.6%
C 662256
8.6%
D 661607
8.5%
U 155224
 
2.0%
Other values (10) 344591
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7740328
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1333761
17.2%
N 1094408
14.1%
O 818339
10.6%
669544
8.7%
T 669414
8.6%
S 668928
8.6%
F 662256
8.6%
C 662256
8.6%
D 661607
8.5%
U 155224
 
2.0%
Other values (10) 344591
 
4.5%

REPORT_TYPE
Categorical

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing24314
Missing (%)3.0%
Memory size6.2 MiB
NOT ON SCENE (DESK REPORT)
447696 
ON SCENE
345473 
AMENDED
 
240

Length

Max length26
Median length26
Mean length18.156537
Min length7

Characters and Unicode

Total characters14405560
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowON SCENE
2nd rowON SCENE
3rd rowON SCENE
4th rowON SCENE
5th rowON SCENE

Common Values

ValueCountFrequency (%)
NOT ON SCENE (DESK REPORT) 447696
54.7%
ON SCENE 345473
42.2%
AMENDED 240
 
< 0.1%
(Missing) 24314
 
3.0%

Length

2024-04-07T23:25:47.419742image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T23:25:47.489873image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
on 793169
27.1%
scene 793169
27.1%
not 447696
15.3%
desk 447696
15.3%
report 447696
15.3%
amended 240
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 2482210
17.2%
2136257
14.8%
N 2034274
14.1%
O 1688561
11.7%
S 1240865
8.6%
T 895392
 
6.2%
R 895392
 
6.2%
C 793169
 
5.5%
D 448176
 
3.1%
( 447696
 
3.1%
Other values (5) 1343568
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14405560
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 2482210
17.2%
2136257
14.8%
N 2034274
14.1%
O 1688561
11.7%
S 1240865
8.6%
T 895392
 
6.2%
R 895392
 
6.2%
C 793169
 
5.5%
D 448176
 
3.1%
( 447696
 
3.1%
Other values (5) 1343568
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14405560
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 2482210
17.2%
2136257
14.8%
N 2034274
14.1%
O 1688561
11.7%
S 1240865
8.6%
T 895392
 
6.2%
R 895392
 
6.2%
C 793169
 
5.5%
D 448176
 
3.1%
( 447696
 
3.1%
Other values (5) 1343568
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14405560
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 2482210
17.2%
2136257
14.8%
N 2034274
14.1%
O 1688561
11.7%
S 1240865
8.6%
T 895392
 
6.2%
R 895392
 
6.2%
C 793169
 
5.5%
D 448176
 
3.1%
( 447696
 
3.1%
Other values (5) 1343568
9.3%

CRASH_TYPE
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
NO INJURY / DRIVE AWAY
599383 
INJURY AND / OR TOW DUE TO CRASH
218340 

Length

Max length32
Median length22
Mean length24.670097
Min length22

Characters and Unicode

Total characters20173306
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINJURY AND / OR TOW DUE TO CRASH
2nd rowNO INJURY / DRIVE AWAY
3rd rowINJURY AND / OR TOW DUE TO CRASH
4th rowINJURY AND / OR TOW DUE TO CRASH
5th rowNO INJURY / DRIVE AWAY

Common Values

ValueCountFrequency (%)
NO INJURY / DRIVE AWAY 599383
73.3%
INJURY AND / OR TOW DUE TO CRASH 218340
 
26.7%

Length

2024-04-07T23:25:47.548537image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T23:25:47.615231image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
injury 817723
17.2%
817723
17.2%
no 599383
12.6%
drive 599383
12.6%
away 599383
12.6%
and 218340
 
4.6%
or 218340
 
4.6%
tow 218340
 
4.6%
due 218340
 
4.6%
to 218340
 
4.6%

Most occurring characters

ValueCountFrequency (%)
3925912
19.5%
R 1853786
9.2%
N 1635446
 
8.1%
A 1635446
 
8.1%
Y 1417106
 
7.0%
I 1417106
 
7.0%
O 1254403
 
6.2%
D 1036063
 
5.1%
U 1036063
 
5.1%
/ 817723
 
4.1%
Other values (8) 4144252
20.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20173306
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3925912
19.5%
R 1853786
9.2%
N 1635446
 
8.1%
A 1635446
 
8.1%
Y 1417106
 
7.0%
I 1417106
 
7.0%
O 1254403
 
6.2%
D 1036063
 
5.1%
U 1036063
 
5.1%
/ 817723
 
4.1%
Other values (8) 4144252
20.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20173306
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3925912
19.5%
R 1853786
9.2%
N 1635446
 
8.1%
A 1635446
 
8.1%
Y 1417106
 
7.0%
I 1417106
 
7.0%
O 1254403
 
6.2%
D 1036063
 
5.1%
U 1036063
 
5.1%
/ 817723
 
4.1%
Other values (8) 4144252
20.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20173306
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3925912
19.5%
R 1853786
9.2%
N 1635446
 
8.1%
A 1635446
 
8.1%
Y 1417106
 
7.0%
I 1417106
 
7.0%
O 1254403
 
6.2%
D 1036063
 
5.1%
U 1036063
 
5.1%
/ 817723
 
4.1%
Other values (8) 4144252
20.5%

INTERSECTION_RELATED_I
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing630174
Missing (%)77.1%
Memory size1.6 MiB
True
178642 
False
 
8907
(Missing)
630174 
ValueCountFrequency (%)
True 178642
 
21.8%
False 8907
 
1.1%
(Missing) 630174
77.1%
2024-04-07T23:25:47.676090image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

NOT_RIGHT_OF_WAY_I
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing780015
Missing (%)95.4%
Memory size1.6 MiB
True
 
34256
False
 
3452
(Missing)
780015 
ValueCountFrequency (%)
True 34256
 
4.2%
False 3452
 
0.4%
(Missing) 780015
95.4%
2024-04-07T23:25:47.732949image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

HIT_AND_RUN_I
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing561774
Missing (%)68.7%
Memory size1.6 MiB
True
244955 
False
 
10994
(Missing)
561774 
ValueCountFrequency (%)
True 244955
30.0%
False 10994
 
1.3%
(Missing) 561774
68.7%
2024-04-07T23:25:47.876810image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

DAMAGE
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
OVER $1,500
507718 
$501 - $1,500
216632 
$500 OR LESS
93373 

Length

Max length13
Median length11
Mean length11.644029
Min length11

Characters and Unicode

Total characters9521590
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOVER $1,500
2nd rowOVER $1,500
3rd row$501 - $1,500
4th rowOVER $1,500
5th rowOVER $1,500

Common Values

ValueCountFrequency (%)
OVER $1,500 507718
62.1%
$501 - $1,500 216632
26.5%
$500 OR LESS 93373
 
11.4%

Length

2024-04-07T23:25:47.930507image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T23:25:48.070553image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1,500 724350
37.2%
over 507718
26.1%
501 216632
 
11.1%
216632
 
11.1%
500 93373
 
4.8%
or 93373
 
4.8%
less 93373
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 1852078
19.5%
1127728
11.8%
$ 1034355
10.9%
5 1034355
10.9%
1 940982
9.9%
, 724350
 
7.6%
O 601091
 
6.3%
E 601091
 
6.3%
R 601091
 
6.3%
V 507718
 
5.3%
Other values (3) 496751
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9521590
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1852078
19.5%
1127728
11.8%
$ 1034355
10.9%
5 1034355
10.9%
1 940982
9.9%
, 724350
 
7.6%
O 601091
 
6.3%
E 601091
 
6.3%
R 601091
 
6.3%
V 507718
 
5.3%
Other values (3) 496751
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9521590
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1852078
19.5%
1127728
11.8%
$ 1034355
10.9%
5 1034355
10.9%
1 940982
9.9%
, 724350
 
7.6%
O 601091
 
6.3%
E 601091
 
6.3%
R 601091
 
6.3%
V 507718
 
5.3%
Other values (3) 496751
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9521590
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1852078
19.5%
1127728
11.8%
$ 1034355
10.9%
5 1034355
10.9%
1 940982
9.9%
, 724350
 
7.6%
O 601091
 
6.3%
E 601091
 
6.3%
R 601091
 
6.3%
V 507718
 
5.3%
Other values (3) 496751
 
5.2%
Distinct620545
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
Minimum2013-06-01 20:31:00
Maximum2024-03-26 01:42:00
2024-04-07T23:25:48.142984image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:48.220370image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
UNABLE TO DETERMINE
318311 
FAILING TO YIELD RIGHT-OF-WAY
89872 
FOLLOWING TOO CLOSELY
79457 
NOT APPLICABLE
43275 
IMPROPER OVERTAKING/PASSING
40275 
Other values (35)
246533 

Length

Max length80
Median length75
Mean length23.717536
Min length6

Characters and Unicode

Total characters19394375
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFOLLOWING TOO CLOSELY
2nd rowFAILING TO REDUCE SPEED TO AVOID CRASH
3rd rowFAILING TO REDUCE SPEED TO AVOID CRASH
4th rowUNABLE TO DETERMINE
5th rowFOLLOWING TOO CLOSELY

Common Values

ValueCountFrequency (%)
UNABLE TO DETERMINE 318311
38.9%
FAILING TO YIELD RIGHT-OF-WAY 89872
 
11.0%
FOLLOWING TOO CLOSELY 79457
 
9.7%
NOT APPLICABLE 43275
 
5.3%
IMPROPER OVERTAKING/PASSING 40275
 
4.9%
FAILING TO REDUCE SPEED TO AVOID CRASH 34546
 
4.2%
IMPROPER BACKING 32183
 
3.9%
IMPROPER LANE USAGE 29304
 
3.6%
DRIVING SKILLS/KNOWLEDGE/EXPERIENCE 27355
 
3.3%
IMPROPER TURNING/NO SIGNAL 27176
 
3.3%
Other values (30) 95969
 
11.7%

Length

2024-04-07T23:25:48.302423image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
to 479178
17.8%
unable 318311
 
11.8%
determine 318311
 
11.8%
improper 128938
 
4.8%
failing 124418
 
4.6%
yield 90143
 
3.4%
right-of-way 89872
 
3.3%
closely 79457
 
3.0%
too 79457
 
3.0%
following 79457
 
3.0%
Other values (106) 902641
33.6%

Most occurring characters

ValueCountFrequency (%)
E 2379311
12.3%
1872460
 
9.7%
I 1640913
 
8.5%
N 1463185
 
7.5%
O 1408342
 
7.3%
T 1236970
 
6.4%
L 1181395
 
6.1%
R 1119969
 
5.8%
A 1068916
 
5.5%
G 720628
 
3.7%
Other values (23) 5302286
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19394375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 2379311
12.3%
1872460
 
9.7%
I 1640913
 
8.5%
N 1463185
 
7.5%
O 1408342
 
7.3%
T 1236970
 
6.4%
L 1181395
 
6.1%
R 1119969
 
5.8%
A 1068916
 
5.5%
G 720628
 
3.7%
Other values (23) 5302286
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19394375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 2379311
12.3%
1872460
 
9.7%
I 1640913
 
8.5%
N 1463185
 
7.5%
O 1408342
 
7.3%
T 1236970
 
6.4%
L 1181395
 
6.1%
R 1119969
 
5.8%
A 1068916
 
5.5%
G 720628
 
3.7%
Other values (23) 5302286
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19394375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 2379311
12.3%
1872460
 
9.7%
I 1640913
 
8.5%
N 1463185
 
7.5%
O 1408342
 
7.3%
T 1236970
 
6.4%
L 1181395
 
6.1%
R 1119969
 
5.8%
A 1068916
 
5.5%
G 720628
 
3.7%
Other values (23) 5302286
27.3%

SEC_CONTRIBUTORY_CAUSE
Categorical

IMBALANCE 

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
NOT APPLICABLE
335814 
UNABLE TO DETERMINE
295746 
FAILING TO REDUCE SPEED TO AVOID CRASH
 
30501
FAILING TO YIELD RIGHT-OF-WAY
 
25722
DRIVING SKILLS/KNOWLEDGE/EXPERIENCE
 
25124
Other values (35)
104816 

Length

Max length80
Median length75
Mean length19.476758
Min length6

Characters and Unicode

Total characters15926593
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDISTRACTION - FROM INSIDE VEHICLE
2nd rowOPERATING VEHICLE IN ERRATIC, RECKLESS, CARELESS, NEGLIGENT OR AGGRESSIVE MANNER
3rd rowUNABLE TO DETERMINE
4th rowNOT APPLICABLE
5th rowDRIVING SKILLS/KNOWLEDGE/EXPERIENCE

Common Values

ValueCountFrequency (%)
NOT APPLICABLE 335814
41.1%
UNABLE TO DETERMINE 295746
36.2%
FAILING TO REDUCE SPEED TO AVOID CRASH 30501
 
3.7%
FAILING TO YIELD RIGHT-OF-WAY 25722
 
3.1%
DRIVING SKILLS/KNOWLEDGE/EXPERIENCE 25124
 
3.1%
FOLLOWING TOO CLOSELY 21547
 
2.6%
IMPROPER OVERTAKING/PASSING 12508
 
1.5%
IMPROPER LANE USAGE 11477
 
1.4%
WEATHER 9434
 
1.2%
IMPROPER TURNING/NO SIGNAL 8350
 
1.0%
Other values (30) 41500
 
5.1%

Length

2024-04-07T23:25:48.383034image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
to 383293
16.7%
not 336812
14.7%
applicable 335814
14.7%
unable 295746
12.9%
determine 295746
12.9%
failing 56223
 
2.5%
improper 38903
 
1.7%
speed 33412
 
1.5%
reduce 30501
 
1.3%
avoid 30501
 
1.3%
Other values (106) 451894
19.7%

Most occurring characters

ValueCountFrequency (%)
E 2092865
13.1%
1471122
 
9.2%
L 1277925
 
8.0%
N 1264704
 
7.9%
A 1252400
 
7.9%
I 1174220
 
7.4%
T 1157151
 
7.3%
O 1032677
 
6.5%
P 833672
 
5.2%
B 648046
 
4.1%
Other values (23) 3721811
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15926593
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 2092865
13.1%
1471122
 
9.2%
L 1277925
 
8.0%
N 1264704
 
7.9%
A 1252400
 
7.9%
I 1174220
 
7.4%
T 1157151
 
7.3%
O 1032677
 
6.5%
P 833672
 
5.2%
B 648046
 
4.1%
Other values (23) 3721811
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15926593
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 2092865
13.1%
1471122
 
9.2%
L 1277925
 
8.0%
N 1264704
 
7.9%
A 1252400
 
7.9%
I 1174220
 
7.4%
T 1157151
 
7.3%
O 1032677
 
6.5%
P 833672
 
5.2%
B 648046
 
4.1%
Other values (23) 3721811
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15926593
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 2092865
13.1%
1471122
 
9.2%
L 1277925
 
8.0%
N 1264704
 
7.9%
A 1252400
 
7.9%
I 1174220
 
7.4%
T 1157151
 
7.3%
O 1032677
 
6.5%
P 833672
 
5.2%
B 648046
 
4.1%
Other values (23) 3721811
23.4%

STREET_NO
Real number (ℝ)

Distinct11728
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3689.6337
Minimum0
Maximum451100
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-04-07T23:25:48.461555image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile150
Q11250
median3201
Q35600
95-th percentile9041
Maximum451100
Range451100
Interquartile range (IQR)4350

Descriptive statistics

Standard deviation2886.8844
Coefficient of variation (CV)0.78243115
Kurtosis705.36663
Mean3689.6337
Median Absolute Deviation (MAD)2101
Skewness5.2745308
Sum3.0170984 × 109
Variance8334101.4
MonotonicityNot monotonic
2024-04-07T23:25:48.531854image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1600 5262
 
0.6%
100 4886
 
0.6%
800 4779
 
0.6%
200 4525
 
0.6%
300 4114
 
0.5%
2400 3998
 
0.5%
4700 3997
 
0.5%
1200 3927
 
0.5%
500 3924
 
0.5%
6300 3796
 
0.5%
Other values (11718) 774515
94.7%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 3418
0.4%
2 1660
0.2%
3 667
 
0.1%
4 144
 
< 0.1%
5 599
 
0.1%
6 202
 
< 0.1%
7 166
 
< 0.1%
8 194
 
< 0.1%
9 175
 
< 0.1%
ValueCountFrequency (%)
451100 1
 
< 0.1%
34453 1
 
< 0.1%
13799 5
 
< 0.1%
13795 1
 
< 0.1%
13787 1
 
< 0.1%
13781 1
 
< 0.1%
13780 1
 
< 0.1%
13770 27
< 0.1%
13768 1
 
< 0.1%
13763 1
 
< 0.1%

STREET_DIRECTION
Categorical

Distinct4
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size6.2 MiB
W
292260 
S
273919 
N
195919 
E
55621 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters817719
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowS
3rd rowN
4th rowW
5th rowW

Common Values

ValueCountFrequency (%)
W 292260
35.7%
S 273919
33.5%
N 195919
24.0%
E 55621
 
6.8%
(Missing) 4
 
< 0.1%

Length

2024-04-07T23:25:48.594687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T23:25:48.660533image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
w 292260
35.7%
s 273919
33.5%
n 195919
24.0%
e 55621
 
6.8%

Most occurring characters

ValueCountFrequency (%)
W 292260
35.7%
S 273919
33.5%
N 195919
24.0%
E 55621
 
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 817719
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 292260
35.7%
S 273919
33.5%
N 195919
24.0%
E 55621
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 817719
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 292260
35.7%
S 273919
33.5%
N 195919
24.0%
E 55621
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 817719
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 292260
35.7%
S 273919
33.5%
N 195919
24.0%
E 55621
 
6.8%
Distinct1641
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Memory size6.2 MiB
2024-04-07T23:25:48.790305image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length31
Median length24
Mean length10.678632
Min length4

Characters and Unicode

Total characters8732152
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique141 ?
Unique (%)< 0.1%

Sample

1st rowOHARE ST
2nd rowASHLAND AVE
3rd rowLONG AVE
4th rowTERMINAL ST
5th rowTERMINAL ST
ValueCountFrequency (%)
ave 414428
23.7%
st 254107
 
14.5%
rd 52778
 
3.0%
dr 50039
 
2.9%
blvd 31182
 
1.8%
lake 26020
 
1.5%
western 23756
 
1.4%
shore 21868
 
1.3%
pulaski 19695
 
1.1%
cicero 18335
 
1.0%
Other values (1344) 835757
47.8%
2024-04-07T23:25:49.017248image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 1000603
11.5%
A 953307
10.9%
930244
 
10.7%
T 620128
 
7.1%
S 576159
 
6.6%
R 559100
 
6.4%
V 506123
 
5.8%
N 438388
 
5.0%
L 407720
 
4.7%
O 365313
 
4.2%
Other values (30) 2375067
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8732152
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1000603
11.5%
A 953307
10.9%
930244
 
10.7%
T 620128
 
7.1%
S 576159
 
6.6%
R 559100
 
6.4%
V 506123
 
5.8%
N 438388
 
5.0%
L 407720
 
4.7%
O 365313
 
4.2%
Other values (30) 2375067
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8732152
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1000603
11.5%
A 953307
10.9%
930244
 
10.7%
T 620128
 
7.1%
S 576159
 
6.6%
R 559100
 
6.4%
V 506123
 
5.8%
N 438388
 
5.0%
L 407720
 
4.7%
O 365313
 
4.2%
Other values (30) 2375067
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8732152
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1000603
11.5%
A 953307
10.9%
930244
 
10.7%
T 620128
 
7.1%
S 576159
 
6.6%
R 559100
 
6.4%
V 506123
 
5.8%
N 438388
 
5.0%
L 407720
 
4.7%
O 365313
 
4.2%
Other values (30) 2375067
27.2%

BEAT_OF_OCCURRENCE
Real number (ℝ)

Distinct276
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1243.7345
Minimum111
Maximum6100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-04-07T23:25:49.110354image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum111
5-th percentile131
Q1714
median1212
Q31822
95-th percentile2513
Maximum6100
Range5989
Interquartile range (IQR)1108

Descriptive statistics

Standard deviation705.2713
Coefficient of variation (CV)0.56705936
Kurtosis-0.99710383
Mean1243.7345
Median Absolute Deviation (MAD)580
Skewness0.17926577
Sum1.0170241 × 109
Variance497407.61
MonotonicityNot monotonic
2024-04-07T23:25:49.189661image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1834 9942
 
1.2%
114 8369
 
1.0%
813 8217
 
1.0%
815 7798
 
1.0%
1831 7587
 
0.9%
122 7159
 
0.9%
833 6721
 
0.8%
834 6215
 
0.8%
2413 6142
 
0.8%
2512 6007
 
0.7%
Other values (266) 743561
90.9%
ValueCountFrequency (%)
111 3557
0.4%
112 2497
 
0.3%
113 1908
 
0.2%
114 8369
1.0%
121 3831
0.5%
122 7159
0.9%
123 4979
0.6%
124 4277
0.5%
131 4862
0.6%
132 4970
0.6%
ValueCountFrequency (%)
6100 4
 
< 0.1%
2535 2598
0.3%
2534 3472
0.4%
2533 5774
0.7%
2532 2293
 
0.3%
2531 2200
 
0.3%
2525 1791
 
0.2%
2524 2583
0.3%
2523 2860
0.3%
2522 3418
0.4%

PHOTOS_TAKEN_I
Boolean

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing806948
Missing (%)98.7%
Memory size1.6 MiB
True
 
8110
False
 
2665
(Missing)
806948 
ValueCountFrequency (%)
True 8110
 
1.0%
False 2665
 
0.3%
(Missing) 806948
98.7%
2024-04-07T23:25:49.261793image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

STATEMENTS_TAKEN_I
Boolean

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing799465
Missing (%)97.8%
Memory size1.6 MiB
True
 
14874
False
 
3384
(Missing)
799465 
ValueCountFrequency (%)
True 14874
 
1.8%
False 3384
 
0.4%
(Missing) 799465
97.8%
2024-04-07T23:25:49.317629image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

DOORING_I
Boolean

MISSING 

Distinct2
Distinct (%)0.1%
Missing815211
Missing (%)99.7%
Memory size1.6 MiB
True
 
1688
False
 
824
(Missing)
815211 
ValueCountFrequency (%)
True 1688
 
0.2%
False 824
 
0.1%
(Missing) 815211
99.7%
2024-04-07T23:25:49.371847image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

WORK_ZONE_I
Boolean

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing813053
Missing (%)99.4%
Memory size1.6 MiB
True
 
3618
False
 
1052
(Missing)
813053 
ValueCountFrequency (%)
True 3618
 
0.4%
False 1052
 
0.1%
(Missing) 813053
99.4%
2024-04-07T23:25:49.423241image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

WORK_ZONE_TYPE
Categorical

MISSING 

Distinct4
Distinct (%)0.1%
Missing814105
Missing (%)99.6%
Memory size6.2 MiB
CONSTRUCTION
2506 
UNKNOWN
514 
MAINTENANCE
370 
UTILITY
 
228

Length

Max length12
Median length12
Mean length10.872305
Min length7

Characters and Unicode

Total characters39336
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCONSTRUCTION
2nd rowCONSTRUCTION
3rd rowCONSTRUCTION
4th rowCONSTRUCTION
5th rowCONSTRUCTION

Common Values

ValueCountFrequency (%)
CONSTRUCTION 2506
 
0.3%
UNKNOWN 514
 
0.1%
MAINTENANCE 370
 
< 0.1%
UTILITY 228
 
< 0.1%
(Missing) 814105
99.6%

Length

2024-04-07T23:25:49.474889image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T23:25:49.542142image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
construction 2506
69.3%
unknown 514
 
14.2%
maintenance 370
 
10.2%
utility 228
 
6.3%

Most occurring characters

ValueCountFrequency (%)
N 7664
19.5%
T 5838
14.8%
O 5526
14.0%
C 5382
13.7%
I 3332
8.5%
U 3248
8.3%
S 2506
 
6.4%
R 2506
 
6.4%
A 740
 
1.9%
E 740
 
1.9%
Other values (5) 1854
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 7664
19.5%
T 5838
14.8%
O 5526
14.0%
C 5382
13.7%
I 3332
8.5%
U 3248
8.3%
S 2506
 
6.4%
R 2506
 
6.4%
A 740
 
1.9%
E 740
 
1.9%
Other values (5) 1854
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 7664
19.5%
T 5838
14.8%
O 5526
14.0%
C 5382
13.7%
I 3332
8.5%
U 3248
8.3%
S 2506
 
6.4%
R 2506
 
6.4%
A 740
 
1.9%
E 740
 
1.9%
Other values (5) 1854
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 7664
19.5%
T 5838
14.8%
O 5526
14.0%
C 5382
13.7%
I 3332
8.5%
U 3248
8.3%
S 2506
 
6.4%
R 2506
 
6.4%
A 740
 
1.9%
E 740
 
1.9%
Other values (5) 1854
 
4.7%

WORKERS_PRESENT_I
Boolean

MISSING 

Distinct2
Distinct (%)0.2%
Missing816529
Missing (%)99.9%
Memory size1.6 MiB
True
 
1056
False
 
138
(Missing)
816529 
ValueCountFrequency (%)
True 1056
 
0.1%
False 138
 
< 0.1%
(Missing) 816529
99.9%
2024-04-07T23:25:49.602196image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

NUM_UNITS
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0349189
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-04-07T23:25:49.649495image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum18
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.45282316
Coefficient of variation (CV)0.22252639
Kurtosis38.099939
Mean2.0349189
Median Absolute Deviation (MAD)0
Skewness3.3280078
Sum1664000
Variance0.20504881
MonotonicityNot monotonic
2024-04-07T23:25:49.703998image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 715414
87.5%
3 45230
 
5.5%
1 45221
 
5.5%
4 8745
 
1.1%
5 2130
 
0.3%
6 603
 
0.1%
7 210
 
< 0.1%
8 95
 
< 0.1%
9 38
 
< 0.1%
10 16
 
< 0.1%
Other values (7) 21
 
< 0.1%
ValueCountFrequency (%)
1 45221
 
5.5%
2 715414
87.5%
3 45230
 
5.5%
4 8745
 
1.1%
5 2130
 
0.3%
6 603
 
0.1%
7 210
 
< 0.1%
8 95
 
< 0.1%
9 38
 
< 0.1%
10 16
 
< 0.1%
ValueCountFrequency (%)
18 4
 
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
14 2
 
< 0.1%
13 1
 
< 0.1%
12 5
 
< 0.1%
11 7
 
< 0.1%
10 16
 
< 0.1%
9 38
 
< 0.1%
8 95
< 0.1%

MOST_SEVERE_INJURY
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing1792
Missing (%)0.2%
Memory size6.2 MiB
NO INDICATION OF INJURY
703419 
NONINCAPACITATING INJURY
 
63461
REPORTED, NOT EVIDENT
 
34331
INCAPACITATING INJURY
 
13821
FATAL
 
899

Length

Max length24
Median length23
Mean length22.939915
Min length5

Characters and Unicode

Total characters18717388
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNONINCAPACITATING INJURY
2nd rowNO INDICATION OF INJURY
3rd rowNONINCAPACITATING INJURY
4th rowFATAL
5th rowNO INDICATION OF INJURY

Common Values

ValueCountFrequency (%)
NO INDICATION OF INJURY 703419
86.0%
NONINCAPACITATING INJURY 63461
 
7.8%
REPORTED, NOT EVIDENT 34331
 
4.2%
INCAPACITATING INJURY 13821
 
1.7%
FATAL 899
 
0.1%
(Missing) 1792
 
0.2%

Length

2024-04-07T23:25:49.770747image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T23:25:49.843153image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
injury 780701
25.4%
no 703419
22.9%
indication 703419
22.9%
of 703419
22.9%
nonincapacitating 63461
 
2.1%
reported 34331
 
1.1%
not 34331
 
1.1%
evident 34331
 
1.1%
incapacitating 13821
 
0.4%
fatal 899
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 3241106
17.3%
I 3157135
16.9%
2256201
12.1%
O 2242380
12.0%
T 961875
 
5.1%
A 937063
 
5.0%
C 857983
 
4.6%
R 849363
 
4.5%
U 780701
 
4.2%
Y 780701
 
4.2%
Other values (9) 2652880
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18717388
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 3241106
17.3%
I 3157135
16.9%
2256201
12.1%
O 2242380
12.0%
T 961875
 
5.1%
A 937063
 
5.0%
C 857983
 
4.6%
R 849363
 
4.5%
U 780701
 
4.2%
Y 780701
 
4.2%
Other values (9) 2652880
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18717388
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 3241106
17.3%
I 3157135
16.9%
2256201
12.1%
O 2242380
12.0%
T 961875
 
5.1%
A 937063
 
5.0%
C 857983
 
4.6%
R 849363
 
4.5%
U 780701
 
4.2%
Y 780701
 
4.2%
Other values (9) 2652880
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18717388
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 3241106
17.3%
I 3157135
16.9%
2256201
12.1%
O 2242380
12.0%
T 961875
 
5.1%
A 937063
 
5.0%
C 857983
 
4.6%
R 849363
 
4.5%
U 780701
 
4.2%
Y 780701
 
4.2%
Other values (9) 2652880
14.2%

INJURIES_TOTAL
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)< 0.1%
Missing1780
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.18992381
Minimum0
Maximum21
Zeros703431
Zeros (%)86.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-04-07T23:25:49.984174image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.56603807
Coefficient of variation (CV)2.9803429
Kurtosis45.551048
Mean0.18992381
Median Absolute Deviation (MAD)0
Skewness4.8263445
Sum154967
Variance0.32039909
MonotonicityNot monotonic
2024-04-07T23:25:50.038310image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 703431
86.0%
1 84676
 
10.4%
2 18829
 
2.3%
3 5721
 
0.7%
4 2050
 
0.3%
5 723
 
0.1%
6 289
 
< 0.1%
7 110
 
< 0.1%
8 45
 
< 0.1%
9 26
 
< 0.1%
Other values (10) 43
 
< 0.1%
(Missing) 1780
 
0.2%
ValueCountFrequency (%)
0 703431
86.0%
1 84676
 
10.4%
2 18829
 
2.3%
3 5721
 
0.7%
4 2050
 
0.3%
5 723
 
0.1%
6 289
 
< 0.1%
7 110
 
< 0.1%
8 45
 
< 0.1%
9 26
 
< 0.1%
ValueCountFrequency (%)
21 4
 
< 0.1%
19 1
 
< 0.1%
17 1
 
< 0.1%
16 1
 
< 0.1%
15 7
< 0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 4
 
< 0.1%
11 8
< 0.1%
10 14
< 0.1%

INJURIES_FATAL
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing1780
Missing (%)0.2%
Memory size6.2 MiB
0.0
815044 
1.0
 
836
2.0
 
54
3.0
 
8
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2447829
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 815044
99.7%
1.0 836
 
0.1%
2.0 54
 
< 0.1%
3.0 8
 
< 0.1%
4.0 1
 
< 0.1%
(Missing) 1780
 
0.2%

Length

2024-04-07T23:25:50.094595image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T23:25:50.162052image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 815044
99.9%
1.0 836
 
0.1%
2.0 54
 
< 0.1%
3.0 8
 
< 0.1%
4.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 1630987
66.6%
. 815943
33.3%
1 836
 
< 0.1%
2 54
 
< 0.1%
3 8
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2447829
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1630987
66.6%
. 815943
33.3%
1 836
 
< 0.1%
2 54
 
< 0.1%
3 8
 
< 0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2447829
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1630987
66.6%
. 815943
33.3%
1 836
 
< 0.1%
2 54
 
< 0.1%
3 8
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2447829
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1630987
66.6%
. 815943
33.3%
1 836
 
< 0.1%
2 54
 
< 0.1%
3 8
 
< 0.1%
4 1
 
< 0.1%

INJURIES_INCAPACITATING
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing1780
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.019908253
Minimum0
Maximum10
Zeros801987
Zeros (%)98.1%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-04-07T23:25:50.220573image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.16511357
Coefficient of variation (CV)8.2937243
Kurtosis193.15979
Mean0.019908253
Median Absolute Deviation (MAD)0
Skewness11.32382
Sum16244
Variance0.02726249
MonotonicityNot monotonic
2024-04-07T23:25:50.272694image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 801987
98.1%
1 12271
 
1.5%
2 1269
 
0.2%
3 282
 
< 0.1%
4 98
 
< 0.1%
5 26
 
< 0.1%
6 7
 
< 0.1%
7 1
 
< 0.1%
10 1
 
< 0.1%
8 1
 
< 0.1%
(Missing) 1780
 
0.2%
ValueCountFrequency (%)
0 801987
98.1%
1 12271
 
1.5%
2 1269
 
0.2%
3 282
 
< 0.1%
4 98
 
< 0.1%
5 26
 
< 0.1%
6 7
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
6 7
 
< 0.1%
5 26
 
< 0.1%
4 98
 
< 0.1%
3 282
 
< 0.1%
2 1269
 
0.2%
1 12271
 
1.5%
0 801987
98.1%

INJURIES_NON_INCAPACITATING
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)< 0.1%
Missing1780
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.10711778
Minimum0
Maximum21
Zeros749965
Zeros (%)91.7%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-04-07T23:25:50.329379image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.42226344
Coefficient of variation (CV)3.9420482
Kurtosis79.874379
Mean0.10711778
Median Absolute Deviation (MAD)0
Skewness6.3551825
Sum87402
Variance0.17830641
MonotonicityNot monotonic
2024-04-07T23:25:50.384316image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 749965
91.7%
1 51685
 
6.3%
2 9835
 
1.2%
3 2873
 
0.4%
4 999
 
0.1%
5 348
 
< 0.1%
6 141
 
< 0.1%
7 46
 
< 0.1%
8 22
 
< 0.1%
10 9
 
< 0.1%
Other values (9) 20
 
< 0.1%
(Missing) 1780
 
0.2%
ValueCountFrequency (%)
0 749965
91.7%
1 51685
 
6.3%
2 9835
 
1.2%
3 2873
 
0.4%
4 999
 
0.1%
5 348
 
< 0.1%
6 141
 
< 0.1%
7 46
 
< 0.1%
8 22
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
21 2
 
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
14 1
 
< 0.1%
12 3
 
< 0.1%
11 4
< 0.1%
10 9
< 0.1%
9 6
< 0.1%

INJURIES_REPORTED_NOT_EVIDENT
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing1780
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.061706516
Minimum0
Maximum15
Zeros777695
Zeros (%)95.1%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-04-07T23:25:50.436687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.31924399
Coefficient of variation (CV)5.1735864
Kurtosis96.640062
Mean0.061706516
Median Absolute Deviation (MAD)0
Skewness7.6941999
Sum50349
Variance0.10191673
MonotonicityNot monotonic
2024-04-07T23:25:50.490479image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 777695
95.1%
1 29752
 
3.6%
2 6145
 
0.8%
3 1586
 
0.2%
4 496
 
0.1%
5 172
 
< 0.1%
6 45
 
< 0.1%
7 22
 
< 0.1%
8 11
 
< 0.1%
9 9
 
< 0.1%
Other values (3) 10
 
< 0.1%
(Missing) 1780
 
0.2%
ValueCountFrequency (%)
0 777695
95.1%
1 29752
 
3.6%
2 6145
 
0.8%
3 1586
 
0.2%
4 496
 
0.1%
5 172
 
< 0.1%
6 45
 
< 0.1%
7 22
 
< 0.1%
8 11
 
< 0.1%
9 9
 
< 0.1%
ValueCountFrequency (%)
15 2
 
< 0.1%
11 2
 
< 0.1%
10 6
 
< 0.1%
9 9
 
< 0.1%
8 11
 
< 0.1%
7 22
 
< 0.1%
6 45
 
< 0.1%
5 172
 
< 0.1%
4 496
 
0.1%
3 1586
0.2%

INJURIES_NO_INDICATION
Real number (ℝ)

ZEROS 

Distinct48
Distinct (%)< 0.1%
Missing1780
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean2.0032404
Minimum0
Maximum61
Zeros17119
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-04-07T23:25:50.557620image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum61
Range61
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1571173
Coefficient of variation (CV)0.57762278
Kurtosis69.611204
Mean2.0032404
Median Absolute Deviation (MAD)1
Skewness3.763925
Sum1634530
Variance1.3389205
MonotonicityNot monotonic
2024-04-07T23:25:50.629508image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
2 379375
46.4%
1 250061
30.6%
3 103077
 
12.6%
4 38515
 
4.7%
0 17119
 
2.1%
5 16170
 
2.0%
6 6706
 
0.8%
7 2609
 
0.3%
8 1168
 
0.1%
9 484
 
0.1%
Other values (38) 659
 
0.1%
(Missing) 1780
 
0.2%
ValueCountFrequency (%)
0 17119
 
2.1%
1 250061
30.6%
2 379375
46.4%
3 103077
 
12.6%
4 38515
 
4.7%
5 16170
 
2.0%
6 6706
 
0.8%
7 2609
 
0.3%
8 1168
 
0.1%
9 484
 
0.1%
ValueCountFrequency (%)
61 1
 
< 0.1%
50 1
 
< 0.1%
48 1
 
< 0.1%
46 1
 
< 0.1%
45 2
< 0.1%
43 1
 
< 0.1%
42 3
< 0.1%
40 2
< 0.1%
39 1
 
< 0.1%
38 1
 
< 0.1%

INJURIES_UNKNOWN
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing1780
Missing (%)0.2%
Memory size6.2 MiB
0.0
815943 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2447829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 815943
99.8%
(Missing) 1780
 
0.2%

Length

2024-04-07T23:25:50.696159image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T23:25:50.754141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 815943
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1631886
66.7%
. 815943
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2447829
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1631886
66.7%
. 815943
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2447829
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1631886
66.7%
. 815943
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2447829
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1631886
66.7%
. 815943
33.3%

CRASH_HOUR
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.198073
Minimum0
Maximum23
Zeros17728
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-04-07T23:25:50.803727image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q19
median14
Q317
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.5700507
Coefficient of variation (CV)0.42203514
Kurtosis-0.38952782
Mean13.198073
Median Absolute Deviation (MAD)4
Skewness-0.42698356
Sum10792368
Variance31.025465
MonotonicityNot monotonic
2024-04-07T23:25:50.863942image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
15 63101
 
7.7%
16 62452
 
7.6%
17 60858
 
7.4%
14 54756
 
6.7%
18 50210
 
6.1%
13 49604
 
6.1%
12 47942
 
5.9%
8 43242
 
5.3%
11 41461
 
5.1%
9 37509
 
4.6%
Other values (14) 306588
37.5%
ValueCountFrequency (%)
0 17728
2.2%
1 15210
 
1.9%
2 13085
 
1.6%
3 10681
 
1.3%
4 9509
 
1.2%
5 11295
 
1.4%
6 17792
2.2%
7 34569
4.2%
8 43242
5.3%
9 37509
4.6%
ValueCountFrequency (%)
23 21276
 
2.6%
22 24553
 
3.0%
21 26734
3.3%
20 29896
3.7%
19 37090
4.5%
18 50210
6.1%
17 60858
7.4%
16 62452
7.6%
15 63101
7.7%
14 54756
6.7%

CRASH_DAY_OF_WEEK
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1228167
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-04-07T23:25:50.918883image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9808553
Coefficient of variation (CV)0.48046165
Kurtosis-1.2397911
Mean4.1228167
Median Absolute Deviation (MAD)2
Skewness-0.0772494
Sum3371322
Variance3.9237877
MonotonicityNot monotonic
2024-04-07T23:25:50.968096image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 132905
16.3%
7 121206
14.8%
5 117337
14.3%
3 116478
14.2%
4 115685
14.1%
2 112479
13.8%
1 101633
12.4%
ValueCountFrequency (%)
1 101633
12.4%
2 112479
13.8%
3 116478
14.2%
4 115685
14.1%
5 117337
14.3%
6 132905
16.3%
7 121206
14.8%
ValueCountFrequency (%)
7 121206
14.8%
6 132905
16.3%
5 117337
14.3%
4 115685
14.1%
3 116478
14.2%
2 112479
13.8%
1 101633
12.4%

CRASH_MONTH
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6558235
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-04-07T23:25:51.026401image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4525324
Coefficient of variation (CV)0.51872354
Kurtosis-1.2109649
Mean6.6558235
Median Absolute Deviation (MAD)3
Skewness-0.079796985
Sum5442620
Variance11.91998
MonotonicityNot monotonic
2024-04-07T23:25:51.078281image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 76799
9.4%
9 72415
8.9%
8 70898
8.7%
12 70824
8.7%
11 69260
8.5%
7 68963
8.4%
6 67560
8.3%
5 66530
8.1%
1 66052
8.1%
3 65988
8.1%
Other values (2) 122434
15.0%
ValueCountFrequency (%)
1 66052
8.1%
2 65270
8.0%
3 65988
8.1%
4 57164
7.0%
5 66530
8.1%
6 67560
8.3%
7 68963
8.4%
8 70898
8.7%
9 72415
8.9%
10 76799
9.4%
ValueCountFrequency (%)
12 70824
8.7%
11 69260
8.5%
10 76799
9.4%
9 72415
8.9%
8 70898
8.7%
7 68963
8.4%
6 67560
8.3%
5 66530
8.1%
4 57164
7.0%
3 65988
8.1%

LATITUDE
Real number (ℝ)

SKEWED 

Distinct300091
Distinct (%)37.0%
Missing5615
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean41.854865
Minimum0
Maximum42.02278
Zeros49
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-04-07T23:25:51.146817image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41.712613
Q141.782524
median41.874799
Q341.924392
95-th percentile41.990161
Maximum42.02278
Range42.02278
Interquartile range (IQR)0.1418673

Descriptive statistics

Standard deviation0.33634838
Coefficient of variation (CV)0.008036064
Kurtosis14465.142
Mean41.854865
Median Absolute Deviation (MAD)0.068387089
Skewness-116.26865
Sum33990671
Variance0.11313023
MonotonicityNot monotonic
2024-04-07T23:25:51.221527image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.97620114 1305
 
0.2%
41.90095892 759
 
0.1%
41.79142028 578
 
0.1%
41.7514606 556
 
0.1%
41.72225727 443
 
0.1%
41.75466012 381
 
< 0.1%
41.88085605 329
 
< 0.1%
41.78932932 325
 
< 0.1%
41.90075297 306
 
< 0.1%
41.89680497 304
 
< 0.1%
Other values (300081) 806822
98.7%
(Missing) 5615
 
0.7%
ValueCountFrequency (%)
0 49
< 0.1%
41.64467013 23
< 0.1%
41.64469152 4
 
< 0.1%
41.64469397 7
 
< 0.1%
41.64469408 1
 
< 0.1%
41.64470194 5
 
< 0.1%
41.64471103 1
 
< 0.1%
41.64471232 1
 
< 0.1%
41.64471457 1
 
< 0.1%
41.64471544 4
 
< 0.1%
ValueCountFrequency (%)
42.02277986 9
< 0.1%
42.02275469 1
 
< 0.1%
42.02273632 1
 
< 0.1%
42.02272017 2
 
< 0.1%
42.02266893 1
 
< 0.1%
42.02266114 2
 
< 0.1%
42.02266027 7
< 0.1%
42.02265996 1
 
< 0.1%
42.02265785 1
 
< 0.1%
42.0226457 1
 
< 0.1%

LONGITUDE
Real number (ℝ)

SKEWED 

Distinct300054
Distinct (%)36.9%
Missing5615
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean-87.673537
Minimum-87.936193
Maximum0
Zeros49
Zeros (%)< 0.1%
Negative812059
Negative (%)99.3%
Memory size6.2 MiB
2024-04-07T23:25:51.292615image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-87.936193
5-th percentile-87.776925
Q1-87.72175
median-87.674171
Q3-87.633404
95-th percentile-87.585783
Maximum0
Range87.936193
Interquartile range (IQR)0.088345474

Descriptive statistics

Standard deviation0.68359378
Coefficient of variation (CV)-0.0077970366
Kurtosis16322.547
Mean-87.673537
Median Absolute Deviation (MAD)0.043139996
Skewness127.28991
Sum-71200381
Variance0.46730046
MonotonicityNot monotonic
2024-04-07T23:25:51.362162image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.90530913 1305
 
0.2%
-87.61992817 759
 
0.1%
-87.58014777 578
 
0.1%
-87.58597199 556
 
0.1%
-87.58527557 443
 
0.1%
-87.74138476 381
 
< 0.1%
-87.61763589 329
 
< 0.1%
-87.74164564 325
 
< 0.1%
-87.624235 306
 
< 0.1%
-87.61702742 304
 
< 0.1%
Other values (300044) 806822
98.7%
(Missing) 5615
 
0.7%
ValueCountFrequency (%)
-87.93619295 1
 
< 0.1%
-87.93587692 1
 
< 0.1%
-87.93476313 3
 
< 0.1%
-87.93450972 1
 
< 0.1%
-87.93401422 1
 
< 0.1%
-87.93399393 54
< 0.1%
-87.9339765 3
 
< 0.1%
-87.93302828 8
 
< 0.1%
-87.92822117 3
 
< 0.1%
-87.92726168 13
 
< 0.1%
ValueCountFrequency (%)
0 49
< 0.1%
-87.52458739 13
 
< 0.1%
-87.52458901 4
 
< 0.1%
-87.52464032 1
 
< 0.1%
-87.5246459 1
 
< 0.1%
-87.52467395 8
 
< 0.1%
-87.52467489 1
 
< 0.1%
-87.52467584 1
 
< 0.1%
-87.52467708 1
 
< 0.1%
-87.52468243 1
 
< 0.1%
Distinct300265
Distinct (%)37.0%
Missing5615
Missing (%)0.7%
Memory size6.2 MiB
2024-04-07T23:25:51.595281image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length40
Median length40
Mean length39.779569
Min length11

Characters and Unicode

Total characters32305306
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique190046 ?
Unique (%)23.4%

Sample

1st rowPOINT (-87.665902342962 41.854120262952)
2nd rowPOINT (-87.761883496974 41.942975745006)
3rd rowPOINT (-87.594212812011 41.809781151018)
4th rowPOINT (-87.696642374961 41.899224596015)
5th rowPOINT (-87.585945066953 41.744151639042)
ValueCountFrequency (%)
point 812108
33.3%
41.976201139024 1305
 
0.1%
87.905309125103 1305
 
0.1%
87.619928173678 759
 
< 0.1%
41.900958919109 759
 
< 0.1%
87.580147768689 578
 
< 0.1%
41.791420282098 578
 
< 0.1%
87.585971992965 556
 
< 0.1%
41.751460603167 556
 
< 0.1%
87.585275565077 443
 
< 0.1%
Other values (600520) 1617377
66.4%
2024-04-07T23:25:51.908113image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 3089155
 
9.6%
8 2891746
 
9.0%
4 2599622
 
8.0%
1 2545102
 
7.9%
6 2307184
 
7.1%
9 2047841
 
6.3%
5 1864201
 
5.8%
2 1822984
 
5.6%
3 1785259
 
5.5%
1624216
 
5.0%
Other values (10) 9727996
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32305306
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 3089155
 
9.6%
8 2891746
 
9.0%
4 2599622
 
8.0%
1 2545102
 
7.9%
6 2307184
 
7.1%
9 2047841
 
6.3%
5 1864201
 
5.8%
2 1822984
 
5.6%
3 1785259
 
5.5%
1624216
 
5.0%
Other values (10) 9727996
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32305306
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 3089155
 
9.6%
8 2891746
 
9.0%
4 2599622
 
8.0%
1 2545102
 
7.9%
6 2307184
 
7.1%
9 2047841
 
6.3%
5 1864201
 
5.8%
2 1822984
 
5.6%
3 1785259
 
5.5%
1624216
 
5.0%
Other values (10) 9727996
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32305306
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 3089155
 
9.6%
8 2891746
 
9.0%
4 2599622
 
8.0%
1 2545102
 
7.9%
6 2307184
 
7.1%
9 2047841
 
6.3%
5 1864201
 
5.8%
2 1822984
 
5.6%
3 1785259
 
5.5%
1624216
 
5.0%
Other values (10) 9727996
30.1%

Interactions

2024-04-07T23:25:34.370966image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:10.340750image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:11.771163image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:12.939197image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:14.480889image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:16.214592image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:18.083105image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:20.131624image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:21.774896image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:23.496901image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:25.226110image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:27.028573image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:28.843654image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:30.671242image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:32.592277image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:34.451332image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:10.399484image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:11.839292image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:13.009811image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:14.562703image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:16.291286image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:18.175812image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:20.205863image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:21.848766image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:23.574864image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:25.303461image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:27.110633image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:28.928312image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:30.755849image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:32.672611image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:34.571824image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:10.482291image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:11.914035image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:13.108282image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:14.695544image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:16.395114image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:18.308967image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:20.322565image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:21.966587image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:23.693977image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:25.432153image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:27.228531image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:29.047532image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:30.877688image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:32.794348image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:34.689283image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:10.570314image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:11.981727image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:13.210215image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:14.815739image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:16.505735image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:18.426155image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:20.427987image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:22.070902image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:23.804870image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:25.549408image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:27.348840image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:29.173607image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:31.006100image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:32.909211image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:34.809843image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:10.655065image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:12.057191image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:13.305256image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:14.948086image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:16.608125image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:18.576842image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:20.542375image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:22.193916image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:23.924720image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:25.675455image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:27.463488image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:29.290103image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:31.126350image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:33.027551image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:34.928040image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:10.755110image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:12.132678image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:13.411708image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:15.067376image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:16.721924image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:18.751254image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:20.653737image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:22.303639image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:24.040929image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:25.803292image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:27.588948image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:29.419531image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:31.262395image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:33.147659image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:35.037496image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:10.851958image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:12.203507image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:13.512604image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:15.191458image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:16.831915image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:18.889747image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:20.761886image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:22.412468image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:24.160214image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:25.925057image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:27.704598image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:29.542114image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:31.401149image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:33.257674image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:35.157858image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:10.958537image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:12.276040image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:13.618261image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:15.313214image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:16.946300image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:19.029478image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:20.870635image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:22.520021image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:24.275942image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:26.049261image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:27.828192image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:29.667526image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:31.541294image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:33.373038image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:35.269274image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:11.062299image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:12.344519image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:13.720501image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:15.424875image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:17.056915image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:19.185232image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:20.979158image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:22.629228image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:24.385769image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:26.169056image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:27.948339image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:29.796574image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:31.684952image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:33.484642image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:35.386150image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:11.176731image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:12.414698image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:13.833092image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:15.541124image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:17.180574image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:19.337059image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:21.089946image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:22.743392image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:24.506684image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:26.291158image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:28.076491image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:29.929356image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:31.827564image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:33.603478image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:35.507270image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:11.276288image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:12.490424image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:13.930371image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:15.657996image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:17.284109image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:19.499458image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:21.211249image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:22.858394image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:24.624810image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:26.427741image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:28.192501image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:30.051061image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:31.963070image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:33.722255image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:35.620387image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:11.373643image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:12.561090image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:14.023592image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:15.769433image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:17.384855image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:19.639310image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:21.320461image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:22.968110image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:24.742138image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:26.554007image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:28.300354image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:30.168110image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:32.085062image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:33.831307image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:35.739671image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:11.475644image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:12.637031image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:14.126202image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:15.889903image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:17.639550image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:19.784716image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:21.440571image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:23.082368image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:24.864262image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:26.680736image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:28.495818image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:30.289084image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:32.213564image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:33.951245image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:35.852304image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:11.588532image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:12.706962image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:14.237017image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:16.002392image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:17.792674image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:19.904672image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:21.550865image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:23.280555image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:24.982406image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:26.798034image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:28.614834image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:30.422257image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:32.340758image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:34.146440image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:35.965144image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:11.698414image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:12.844995image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:14.351712image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:16.113301image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:17.937716image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:20.015776image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:21.659218image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:23.385651image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:25.095407image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:26.914087image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:28.734477image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:30.553220image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:32.472576image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T23:25:34.257296image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Missing values

2024-04-07T23:25:36.619343image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-07T23:25:38.894988image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-04-07T23:25:43.566849image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CRASH_RECORD_IDCRASH_DATE_EST_ICRASH_DATEPOSTED_SPEED_LIMITTRAFFIC_CONTROL_DEVICEDEVICE_CONDITIONWEATHER_CONDITIONLIGHTING_CONDITIONFIRST_CRASH_TYPETRAFFICWAY_TYPELANE_CNTALIGNMENTROADWAY_SURFACE_CONDROAD_DEFECTREPORT_TYPECRASH_TYPEINTERSECTION_RELATED_INOT_RIGHT_OF_WAY_IHIT_AND_RUN_IDAMAGEDATE_POLICE_NOTIFIEDPRIM_CONTRIBUTORY_CAUSESEC_CONTRIBUTORY_CAUSESTREET_NOSTREET_DIRECTIONSTREET_NAMEBEAT_OF_OCCURRENCEPHOTOS_TAKEN_ISTATEMENTS_TAKEN_IDOORING_IWORK_ZONE_IWORK_ZONE_TYPEWORKERS_PRESENT_INUM_UNITSMOST_SEVERE_INJURYINJURIES_TOTALINJURIES_FATALINJURIES_INCAPACITATINGINJURIES_NON_INCAPACITATINGINJURIES_REPORTED_NOT_EVIDENTINJURIES_NO_INDICATIONINJURIES_UNKNOWNCRASH_HOURCRASH_DAY_OF_WEEKCRASH_MONTHLATITUDELONGITUDELOCATION
06c1659069e9c6285a650e70d6f9b574ed5f64c12888479093dfeef179c0344ec6d2057eae224b5c0d5dfc278c0a237f8c22543f07fdef2e4a95a3849871c9345NaN08/18/2023 12:50:00 PM15OTHERFUNCTIONING PROPERLYCLEARDAYLIGHTREAR ENDOTHERNaNSTRAIGHT AND LEVELDRYNO DEFECTSON SCENEINJURY AND / OR TOW DUE TO CRASHNaNNaNNaNOVER $1,50008/18/2023 12:55:00 PMFOLLOWING TOO CLOSELYDISTRACTION - FROM INSIDE VEHICLE700WOHARE ST1654.0NaNNaNNaNNaNNaNNaN2NONINCAPACITATING INJURY1.00.00.01.00.01.00.01268NaNNaNNaN
15f54a59fcb087b12ae5b1acff96a3caf4f2d37e79f8db4106558b34b8a6d2b81af02cf91b576ecd7ced08ffd10fcfd940a84f7613125b89d33636e6075064e22NaN07/29/2023 02:45:00 PM30TRAFFIC SIGNALFUNCTIONING PROPERLYCLEARDAYLIGHTPARKED MOTOR VEHICLEDIVIDED - W/MEDIAN (NOT RAISED)NaNSTRAIGHT AND LEVELDRYNO DEFECTSON SCENENO INJURY / DRIVE AWAYNaNNaNYOVER $1,50007/29/2023 02:45:00 PMFAILING TO REDUCE SPEED TO AVOID CRASHOPERATING VEHICLE IN ERRATIC, RECKLESS, CARELESS, NEGLIGENT OR AGGRESSIVE MANNER2101SASHLAND AVE1235.0NaNNaNNaNNaNNaNNaN4NO INDICATION OF INJURY0.00.00.00.00.01.00.0147741.854120-87.665902POINT (-87.665902342962 41.854120262952)
261fcb8c1eb522a6469b460e2134df3d15f82e81fd93e9cafd3dc7e631b9e1ba8b450a63af12bd90d1d2d9b127ea287f88d32e138a4eeba17799f536c08048934NaN08/18/2023 05:58:00 PM30NO CONTROLSNO CONTROLSCLEARDAYLIGHTPEDALCYCLISTNOT DIVIDEDNaNSTRAIGHT AND LEVELDRYNO DEFECTSON SCENEINJURY AND / OR TOW DUE TO CRASHNaNNaNNaN$501 - $1,50008/18/2023 06:01:00 PMFAILING TO REDUCE SPEED TO AVOID CRASHUNABLE TO DETERMINE3422NLONG AVE1633.0NaNNaNNaNNaNNaNNaN2NONINCAPACITATING INJURY1.00.00.01.00.01.00.0176841.942976-87.761883POINT (-87.761883496974 41.942975745006)
3004cd14d0303a9163aad69a2d7f341b7da2a8572b2ab3378594bfae8ac53dcb604dd8d414f93c290b55862f9f2517ad32e6209cbc8034c2e26eb3c2bc9724390NaN11/26/2019 08:38:00 AM25NO CONTROLSNO CONTROLSCLEARDAYLIGHTPEDESTRIANONE-WAYNaNCURVE ON GRADEDRYNO DEFECTSON SCENEINJURY AND / OR TOW DUE TO CRASHNaNNaNNaNOVER $1,50011/26/2019 08:38:00 AMUNABLE TO DETERMINENOT APPLICABLE5WTERMINAL ST1655.0YYNaNNaNNaNNaN2FATAL1.01.00.00.00.01.00.08311NaNNaNNaN
4a1d5f0ea90897745365a4cbb06cc60329a120d89753fac2b02d69c9685d9cf7c763870a60abd01484a39ed1e6c09b1ba59f38214c03a83cccde1247f794e0287NaN08/18/2023 10:45:00 AM20NO CONTROLSNO CONTROLSCLEARDAYLIGHTFIXED OBJECTOTHERNaNSTRAIGHT AND LEVELDRYNO DEFECTSON SCENENO INJURY / DRIVE AWAYNaNNaNNaNOVER $1,50008/18/2023 10:48:00 AMFOLLOWING TOO CLOSELYDRIVING SKILLS/KNOWLEDGE/EXPERIENCE3WTERMINAL ST1653.0NaNNaNNaNNaNNaNNaN1NO INDICATION OF INJURY0.00.00.00.00.01.00.01068NaNNaNNaN
5b236c77d59e32b7b469a6e2f17f438b7457e1bd8bc689b14cb4f5b1590cbe784f2b9e554b41925797251cbd3e93a3f4a131d1923b327673d441ae79c052f79c2NaN07/29/2023 01:00:00 PM30TRAFFIC SIGNALFUNCTIONING PROPERLYCLEARDAYLIGHTTURNINGNOT DIVIDEDNaNSTRAIGHT AND LEVELUNKNOWNUNKNOWNNOT ON SCENE (DESK REPORT)NO INJURY / DRIVE AWAYYNaNNaN$501 - $1,50007/29/2023 01:46:00 PMUNABLE TO DETERMINEUNABLE TO DETERMINE1732NLA SALLE DR1814.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.03.00.01377NaNNaNNaN
635156ce97cab22747495e92e8bbb16c57e0e60dc3ce6d1f1852f2f7cece07c7ae825b073b286b1da52dfa58082ff6d763ecf1f13f06a223c7aed2b6c1e8c5972NaN02/06/2023 05:30:00 PM30NO CONTROLSNO CONTROLSCLEARDARKNESS, LIGHTED ROADREAR ENDONE-WAYNaNCURVE, LEVELDRYNO DEFECTSON SCENENO INJURY / DRIVE AWAYNaNNaNNaN$501 - $1,50002/06/2023 05:35:00 PMUNABLE TO DETERMINEUNABLE TO DETERMINE2WTERMINAL ST1652.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.02.00.01722NaNNaNNaN
70e208d23344f0d1b3a9fcd4bb07676a750ddb73c397b5c398a33743fd0d49b8ce737c7740b1a77cdaebd61e8e79bddb284452d744b16668f9777f256eec28ff5NaN08/13/2023 01:30:00 PM35NO CONTROLSFUNCTIONING PROPERLYCLEARDAYLIGHTANGLEOTHERNaNSTRAIGHT AND LEVELDRYNO DEFECTSNOT ON SCENE (DESK REPORT)NO INJURY / DRIVE AWAYNaNNaNNaNOVER $1,50008/13/2023 07:40:00 PMIMPROPER BACKINGUNABLE TO DETERMINE9000SPROSPECT AVE2221.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.02.00.01318NaNNaNNaN
814386daec6219c6032b71612b28f0e4cd38e2898f39aae84deeb4efaf165253548cb27cb90088b2d4f9d3e484aa1e7f5076969da4d3b8137a43a3dc94536ac19NaN08/13/2023 12:11:00 AM30TRAFFIC SIGNALFUNCTIONING PROPERLYCLEARDARKNESS, LIGHTED ROADTURNINGFOUR WAYNaNSTRAIGHT AND LEVELDRYNO DEFECTSON SCENENO INJURY / DRIVE AWAYYNaNNaNOVER $1,50008/13/2023 12:11:00 AMIMPROPER TURNING/NO SIGNALUNABLE TO DETERMINE5900SWENTWORTH AVE232.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.02.00.0018NaNNaNNaN
9359bf9f5872d646bb63576e55b1e0b480dc93c2b935ab571dc26ddb48b7a328fbfe130ae70bbff9f03787041b6fb029ba02529da9a1f57494e385ec0e13ed834NaN01/31/2022 07:45:00 PM25NO CONTROLSNO CONTROLSCLEARDARKNESSREAR ENDONE-WAYNaNSTRAIGHT AND LEVELDRYNO DEFECTSNOT ON SCENE (DESK REPORT)NO INJURY / DRIVE AWAYNaNNaNY$501 - $1,50001/31/2022 07:58:00 PMNOT APPLICABLENOT APPLICABLE4546WGLADYS AVE1131.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.05.00.01921NaNNaNNaN
CRASH_RECORD_IDCRASH_DATE_EST_ICRASH_DATEPOSTED_SPEED_LIMITTRAFFIC_CONTROL_DEVICEDEVICE_CONDITIONWEATHER_CONDITIONLIGHTING_CONDITIONFIRST_CRASH_TYPETRAFFICWAY_TYPELANE_CNTALIGNMENTROADWAY_SURFACE_CONDROAD_DEFECTREPORT_TYPECRASH_TYPEINTERSECTION_RELATED_INOT_RIGHT_OF_WAY_IHIT_AND_RUN_IDAMAGEDATE_POLICE_NOTIFIEDPRIM_CONTRIBUTORY_CAUSESEC_CONTRIBUTORY_CAUSESTREET_NOSTREET_DIRECTIONSTREET_NAMEBEAT_OF_OCCURRENCEPHOTOS_TAKEN_ISTATEMENTS_TAKEN_IDOORING_IWORK_ZONE_IWORK_ZONE_TYPEWORKERS_PRESENT_INUM_UNITSMOST_SEVERE_INJURYINJURIES_TOTALINJURIES_FATALINJURIES_INCAPACITATINGINJURIES_NON_INCAPACITATINGINJURIES_REPORTED_NOT_EVIDENTINJURIES_NO_INDICATIONINJURIES_UNKNOWNCRASH_HOURCRASH_DAY_OF_WEEKCRASH_MONTHLATITUDELONGITUDELOCATION
8177135ee5d998db696abf1c85925f71d82015adb33f3b2cdad1942d7d5cf7f077d3ae22547160f024f0ebd1d4a6875fc58fd1e824aa2d1e0a9c53db3cb3d86efb3038NaN06/30/2023 09:37:00 PM30NO CONTROLSNO CONTROLSCLEARDARKNESS, LIGHTED ROADPARKED MOTOR VEHICLEDIVIDED - W/MEDIAN (NOT RAISED)NaNSTRAIGHT AND LEVELDRYNO DEFECTSNaNINJURY AND / OR TOW DUE TO CRASHNaNNaNNaNOVER $1,50006/30/2023 09:38:00 PMUNABLE TO DETERMINEUNABLE TO DETERMINE7559SUNION AVE621.0NaNNaNNaNNaNNaNNaN2INCAPACITATING INJURY1.00.01.00.00.00.00.0216641.756217-87.641596POINT (-87.641596011467 41.756217389831)
8177143a5f25711e9162f0042a53337a25f2c3dc72893c1172d9320ac6736361e1e92ac801e9efe5515c324128870fc638b9a709c76a9773940e934a87ac3230fba3d8NaN11/22/2019 07:30:00 AM30TRAFFIC SIGNALFUNCTIONING PROPERLYCLEARDAYLIGHTREAR TO FRONTDIVIDED - W/MEDIAN (NOT RAISED)NaNSTRAIGHT AND LEVELDRYNO DEFECTSNaNNO INJURY / DRIVE AWAYNaNNaNYOVER $1,50011/22/2019 09:15:00 AMIMPROPER BACKINGUNABLE TO DETERMINE3959NMONTICELLO AVE1732.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.04.00.0761141.953655-87.718926POINT (-87.718925716094 41.953654536112)
817715790a72494289861e0d12786c57659df0fcc3ff90df83a6cb1de9271f989813436a7f8ae113e075d3b5e5bd517e4495c072d513638702c6f4cc134f156226c932Y09/08/2020 03:10:00 PM30NO CONTROLSNO CONTROLSRAINDUSKPARKED MOTOR VEHICLENOT DIVIDEDNaNSTRAIGHT AND LEVELWETNO DEFECTSNaNNO INJURY / DRIVE AWAYNaNNaNY$501 - $1,50009/08/2020 06:19:00 PMUNABLE TO DETERMINENOT APPLICABLE130E34TH ST211.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.01.00.0153941.833010-87.621878POINT (-87.621877672023 41.833010486005)
8177165c36f4e91b3deefd051dee799c3957b198389d8fe01247dc7607ce56bd783470260b8a7eaba4d7d9c5e58f44e59ca37b5717088a0e3ebd46a964608fd06a6e14NaN01/06/2020 01:03:00 PM30NO CONTROLSNO CONTROLSCLEARDAYLIGHTANGLENOT DIVIDEDNaNSTRAIGHT AND LEVELDRYNO DEFECTSNaNINJURY AND / OR TOW DUE TO CRASHNaNNaNNaNOVER $1,50001/06/2020 01:03:00 PMNOT APPLICABLENOT APPLICABLE5635WCORCORAN PL1512.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.02.00.0132141.887091-87.766594POINT (-87.766594246183 41.887090677713)
817717fcf3c5ddd8ba79a1b43c039a2507b201c999d659aa85d31dc6c939e84a2a6e4fa066b59b74be162a5758acd4cf8f766b6b242e300b4772466949680c62ae7740NaN04/12/2020 07:10:00 AM30NO CONTROLSNO CONTROLSCLEARDAYLIGHTSIDESWIPE SAME DIRECTIONDIVIDED - W/MEDIAN (NOT RAISED)NaNSTRAIGHT AND LEVELDRYNO DEFECTSNaNNO INJURY / DRIVE AWAYNaNNaNY$501 - $1,50004/12/2020 08:45:00 AMUNABLE TO DETERMINEUNABLE TO DETERMINE2500SLAKE SHORE DR SB133.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.02.00.071441.847421-87.612391POINT (-87.61239120397 41.847420982326)
8177186dee8823d4ae96624b741428681d19f50b5960418b6d790275e76ec34ee74f1b85ea13fab7248a863dc3761b4c1a7d96a18e6c8b1bd5777d665971ec3ab5598cNaN09/02/2023 06:25:00 PM30TRAFFIC SIGNALFUNCTIONING PROPERLYCLEARDAYLIGHTREAR ENDNOT DIVIDEDNaNSTRAIGHT AND LEVELDRYNO DEFECTSNaNNO INJURY / DRIVE AWAYNaNNaNYOVER $1,50009/02/2023 07:46:00 PMFOLLOWING TOO CLOSELYFOLLOWING TOO CLOSELY7500SSTATE ST623.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.02.00.0187941.758092-87.624902POINT (-87.624902228247 41.758092176383)
81771961c8dcd63fae60613bc9ec526fa901420cbe99a6d35840052c27bbd0cf1f8d6af74ff575276d3795f26878601232f6b9297b250a3499b62a96373e068134d21aNaN07/10/2023 12:29:00 PM30TRAFFIC SIGNALFUNCTIONING PROPERLYCLEARDAYLIGHTTURNINGFOUR WAYNaNSTRAIGHT AND LEVELDRYNO DEFECTSNaNNO INJURY / DRIVE AWAYNaNNaNNaNOVER $1,50007/10/2023 01:05:00 PMIMPROPER TURNING/NO SIGNALIMPROPER LANE USAGE1800SUNION AVE1235.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.02.00.0122741.857531-87.644929POINT (-87.644928607359 41.857530859236)
81772054d55bfcc6627f587abbe0d14c42e51b812f930566fb06773f93b4402cb25e01bdba2a8e977644f53be3b63f9abca96a80ff6b36f6e0c0c4e3d6f3efaed136a0NaN12/28/2019 01:16:00 AM35UNKNOWNUNKNOWNCLEARDARKNESS, LIGHTED ROADPARKED MOTOR VEHICLEONE-WAYNaNSTRAIGHT AND LEVELDRYNO DEFECTSNaNINJURY AND / OR TOW DUE TO CRASHNaNNaNYOVER $1,50012/28/2019 01:18:00 AMOPERATING VEHICLE IN ERRATIC, RECKLESS, CARELESS, NEGLIGENT OR AGGRESSIVE MANNERNOT APPLICABLE219W115TH ST522.0NaNNaNNaNNaNNaNNaN5NO INDICATION OF INJURY0.00.00.00.00.01.00.0171241.685142-87.628557POINT (-87.628556919131 41.685141540233)
8177216b6f5ceb4053bfbb3483fb453231caa94ff2351bde4c9d2e9398184ae92621933b7fcecb831289f93a18d8579f23ff5dbae1e25a1bad7dc5f53ee64f45492640NaN10/23/2019 01:32:00 PM30TRAFFIC SIGNALFUNCTIONING PROPERLYCLEARDAYLIGHTPEDESTRIANDIVIDED - W/MEDIAN (NOT RAISED)NaNSTRAIGHT ON GRADEDRYNO DEFECTSNaNINJURY AND / OR TOW DUE TO CRASHNNaNNaN$500 OR LESS10/23/2019 01:36:00 PMRELATED TO BUS STOPNOT APPLICABLE20W79TH ST623.0YYNaNNaNNaNNaN2INCAPACITATING INJURY2.00.02.00.00.00.00.01341041.751046-87.625378POINT (-87.625377942917 41.751045778094)
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